id	sid	tid	token	lemma	pos
app01-9397	1	1	acta	acta	PROPN
app01-9397	1	2	polytechnica	polytechnica	PROPN
app01-9397	1	3	ctu	ctu	NOUN
app01-9397	1	4	proceedings	proceeding	NOUN
app01-9397	1	5	https://doi.org/10.14311/app.2023.42.0032	https://doi.org/10.14311/app.2023.42.0032	PROPN
app01-9397	1	6	acta	acta	PROPN
app01-9397	1	7	polytechnica	polytechnica	PROPN
app01-9397	1	8	ctu	ctu	NOUN
app01-9397	1	9	proceedings	proceeding	NOUN
app01-9397	1	10	42:32–36	42:32–36	PROPN
app01-9397	1	11	,	,	PUNCT
app01-9397	1	12	2023	2023	NUM
app01-9397	1	13	©	©	ADP
app01-9397	1	14	2023	2023	NUM
app01-9397	1	15	the	the	DET
app01-9397	1	16	author(s	author(s	NOUN
app01-9397	1	17	)	)	PUNCT
app01-9397	1	18	.	.	PUNCT
app01-9397	2	1	licensed	license	VERB
app01-9397	2	2	under	under	ADP
app01-9397	2	3	a	a	DET
app01-9397	2	4	cc	cc	NOUN
app01-9397	2	5	-	-	PUNCT
app01-9397	2	6	by	by	ADP
app01-9397	2	7	4.0	4.0	NUM
app01-9397	2	8	licence	licence	NOUN
app01-9397	2	9	published	publish	VERB
app01-9397	2	10	by	by	ADP
app01-9397	2	11	the	the	DET
app01-9397	2	12	czech	czech	PROPN
app01-9397	2	13	technical	technical	PROPN
app01-9397	2	14	university	university	PROPN
app01-9397	2	15	in	in	ADP
app01-9397	2	16	prague	prague	PROPN
app01-9397	2	17	ai	ai	PROPN
app01-9397	2	18	-	-	PUNCT
app01-9397	2	19	assisted	assist	VERB
app01-9397	2	20	study	study	NOUN
app01-9397	2	21	of	of	ADP
app01-9397	2	22	auxetic	auxetic	ADJ
app01-9397	2	23	structures	structure	NOUN
app01-9397	2	24	sergej	sergej	ADJ
app01-9397	2	25	gredneva,∗	gredneva,∗	NOUN
app01-9397	2	26	,	,	PUNCT
app01-9397	2	27	henrik	henrik	PROPN
app01-9397	2	28	s.	s.	PROPN
app01-9397	2	29	steudeb	steudeb	PROPN
app01-9397	2	30	,	,	PUNCT
app01-9397	2	31	stefan	stefan	PROPN
app01-9397	2	32	brondera	brondera	PROPN
app01-9397	2	33	,	,	PUNCT
app01-9397	2	34	oliver	oliver	PROPN
app01-9397	2	35	niggemannb	niggemannb	PROPN
app01-9397	2	36	,	,	PUNCT
app01-9397	2	37	anne	anne	PROPN
app01-9397	2	38	junga	junga	PROPN
app01-9397	2	39	a	a	DET
app01-9397	2	40	helmut	helmut	PROPN
app01-9397	2	41	schmidt	schmidt	PROPN
app01-9397	2	42	university	university	PROPN
app01-9397	2	43	/	/	SYM
app01-9397	2	44	university	university	PROPN
app01-9397	2	45	of	of	ADP
app01-9397	2	46	the	the	DET
app01-9397	2	47	federal	federal	ADJ
app01-9397	2	48	armed	armed	ADJ
app01-9397	2	49	forces	force	NOUN
app01-9397	2	50	hamburg	hamburg	PROPN
app01-9397	2	51	,	,	PUNCT
app01-9397	2	52	professorship	professorship	NOUN
app01-9397	2	53	for	for	ADP
app01-9397	2	54	protective	protective	ADJ
app01-9397	2	55	systems	system	NOUN
app01-9397	2	56	,	,	PUNCT
app01-9397	2	57	holstenhofweg	holstenhofweg	NOUN
app01-9397	2	58	85	85	NUM
app01-9397	2	59	,	,	PUNCT
app01-9397	2	60	22043	22043	NUM
app01-9397	2	61	hamburg	hamburg	PROPN
app01-9397	2	62	,	,	PUNCT
app01-9397	2	63	germany	germany	PROPN
app01-9397	2	64	b	b	PROPN
app01-9397	2	65	helmut	helmut	PROPN
app01-9397	2	66	schmidt	schmidt	PROPN
app01-9397	2	67	university	university	PROPN
app01-9397	2	68	/	/	SYM
app01-9397	2	69	university	university	PROPN
app01-9397	2	70	of	of	ADP
app01-9397	2	71	the	the	DET
app01-9397	2	72	federal	federal	ADJ
app01-9397	2	73	armed	armed	ADJ
app01-9397	2	74	forces	force	NOUN
app01-9397	2	75	hamburg	hamburg	PROPN
app01-9397	2	76	,	,	PUNCT
app01-9397	2	77	professorship	professorship	NOUN
app01-9397	2	78	for	for	ADP
app01-9397	2	79	computer	computer	NOUN
app01-9397	2	80	science	science	NOUN
app01-9397	2	81	in	in	ADP
app01-9397	2	82	mechanical	mechanical	ADJ
app01-9397	2	83	engineering	engineering	NOUN
app01-9397	2	84	,	,	PUNCT
app01-9397	2	85	holstenhofweg	holstenhofweg	NOUN
app01-9397	2	86	85	85	NUM
app01-9397	2	87	,	,	PUNCT
app01-9397	2	88	22043	22043	NUM
app01-9397	2	89	hamburg	hamburg	PROPN
app01-9397	2	90	,	,	PUNCT
app01-9397	2	91	germany	germany	PROPN
app01-9397	2	92	∗	∗	NOUN
app01-9397	2	93	corresponding	correspond	VERB
app01-9397	2	94	author	author	NOUN
app01-9397	2	95	:	:	PUNCT
app01-9397	2	96	sergej.grednev@hsu-hh.de	sergej.grednev@hsu-hh.de	ADJ
app01-9397	2	97	abstract	abstract	NOUN
app01-9397	2	98	.	.	PUNCT
app01-9397	3	1	in	in	ADP
app01-9397	3	2	this	this	DET
app01-9397	3	3	study	study	NOUN
app01-9397	3	4	,	,	PUNCT
app01-9397	3	5	the	the	DET
app01-9397	3	6	viability	viability	NOUN
app01-9397	3	7	of	of	ADP
app01-9397	3	8	using	use	VERB
app01-9397	3	9	machine	machine	NOUN
app01-9397	3	10	learning	learning	NOUN
app01-9397	3	11	models	model	NOUN
app01-9397	3	12	to	to	PART
app01-9397	3	13	predict	predict	VERB
app01-9397	3	14	stress	stress	NOUN
app01-9397	3	15	-	-	PUNCT
app01-9397	3	16	strain	strain	NOUN
app01-9397	3	17	curves	curve	NOUN
app01-9397	3	18	of	of	ADP
app01-9397	3	19	auxetic	auxetic	ADJ
app01-9397	3	20	structures	structure	NOUN
app01-9397	3	21	based	base	VERB
app01-9397	3	22	on	on	ADP
app01-9397	3	23	geometry	geometry	NOUN
app01-9397	3	24	-	-	PUNCT
app01-9397	3	25	describing	describe	VERB
app01-9397	3	26	parameters	parameter	NOUN
app01-9397	3	27	is	be	AUX
app01-9397	3	28	explored	explore	VERB
app01-9397	3	29	.	.	PUNCT
app01-9397	4	1	given	give	VERB
app01-9397	4	2	the	the	DET
app01-9397	4	3	computational	computational	ADJ
app01-9397	4	4	cost	cost	NOUN
app01-9397	4	5	and	and	CCONJ
app01-9397	4	6	time	time	NOUN
app01-9397	4	7	associated	associate	VERB
app01-9397	4	8	with	with	ADP
app01-9397	4	9	generating	generate	VERB
app01-9397	4	10	these	these	DET
app01-9397	4	11	curves	curve	NOUN
app01-9397	4	12	through	through	ADP
app01-9397	4	13	numerical	numerical	ADJ
app01-9397	4	14	simulations	simulation	NOUN
app01-9397	4	15	,	,	PUNCT
app01-9397	4	16	a	a	DET
app01-9397	4	17	machine	machine	NOUN
app01-9397	4	18	learning	learning	NOUN
app01-9397	4	19	-	-	PUNCT
app01-9397	4	20	based	base	VERB
app01-9397	4	21	approach	approach	NOUN
app01-9397	4	22	promises	promise	VERB
app01-9397	4	23	a	a	DET
app01-9397	4	24	more	more	ADV
app01-9397	4	25	efficient	efficient	ADJ
app01-9397	4	26	alternative	alternative	NOUN
app01-9397	4	27	.	.	PUNCT
app01-9397	5	1	a	a	DET
app01-9397	5	2	range	range	NOUN
app01-9397	5	3	of	of	ADP
app01-9397	5	4	machine	machine	NOUN
app01-9397	5	5	learning	learning	NOUN
app01-9397	5	6	models	model	NOUN
app01-9397	5	7	,	,	PUNCT
app01-9397	5	8	including	include	VERB
app01-9397	5	9	artificial	artificial	ADJ
app01-9397	5	10	neural	neural	ADJ
app01-9397	5	11	networks	network	NOUN
app01-9397	5	12	,	,	PUNCT
app01-9397	5	13	k	k	X
app01-9397	5	14	-	-	PUNCT
app01-9397	5	15	nearest	near	ADJ
app01-9397	5	16	neighbors	neighbor	NOUN
app01-9397	5	17	regression	regression	NOUN
app01-9397	5	18	,	,	PUNCT
app01-9397	5	19	support	support	VERB
app01-9397	5	20	vector	vector	NOUN
app01-9397	5	21	regression	regression	NOUN
app01-9397	5	22	,	,	PUNCT
app01-9397	5	23	and	and	CCONJ
app01-9397	5	24	xgboost	xgboost	ADV
app01-9397	5	25	,	,	PUNCT
app01-9397	5	26	is	be	AUX
app01-9397	5	27	implemented	implement	VERB
app01-9397	5	28	and	and	CCONJ
app01-9397	5	29	compared	compare	VERB
app01-9397	5	30	regarding	regard	VERB
app01-9397	5	31	the	the	DET
app01-9397	5	32	aptitude	aptitude	NOUN
app01-9397	5	33	to	to	PART
app01-9397	5	34	predict	predict	VERB
app01-9397	5	35	stress	stress	NOUN
app01-9397	5	36	-	-	PUNCT
app01-9397	5	37	strain	strain	NOUN
app01-9397	5	38	curves	curve	NOUN
app01-9397	5	39	under	under	ADP
app01-9397	5	40	quasi	quasi	ADJ
app01-9397	5	41	-	-	ADJ
app01-9397	5	42	static	static	ADJ
app01-9397	5	43	compressive	compressive	ADJ
app01-9397	5	44	loading	loading	NOUN
app01-9397	5	45	.	.	PUNCT
app01-9397	6	1	training	training	NOUN
app01-9397	6	2	data	datum	NOUN
app01-9397	6	3	is	be	AUX
app01-9397	6	4	generated	generate	VERB
app01-9397	6	5	using	use	VERB
app01-9397	6	6	validated	validate	VERB
app01-9397	6	7	finite	finite	ADJ
app01-9397	6	8	element	element	NOUN
app01-9397	6	9	simulations	simulation	NOUN
app01-9397	6	10	.	.	PUNCT
app01-9397	7	1	the	the	DET
app01-9397	7	2	performance	performance	NOUN
app01-9397	7	3	of	of	ADP
app01-9397	7	4	these	these	DET
app01-9397	7	5	models	model	NOUN
app01-9397	7	6	is	be	AUX
app01-9397	7	7	rigorously	rigorously	ADV
app01-9397	7	8	tested	test	VERB
app01-9397	7	9	on	on	ADP
app01-9397	7	10	data	datum	NOUN
app01-9397	7	11	not	not	PART
app01-9397	7	12	seen	see	VERB
app01-9397	7	13	during	during	ADP
app01-9397	7	14	training	training	NOUN
app01-9397	7	15	.	.	PUNCT
app01-9397	8	1	the	the	DET
app01-9397	8	2	feed	feed	NOUN
app01-9397	8	3	-	-	PUNCT
app01-9397	8	4	forward	forward	ADV
app01-9397	8	5	artificial	artificial	ADJ
app01-9397	8	6	neural	neural	ADJ
app01-9397	8	7	network	network	NOUN
app01-9397	8	8	emerged	emerge	VERB
app01-9397	8	9	as	as	ADP
app01-9397	8	10	the	the	DET
app01-9397	8	11	most	most	ADV
app01-9397	8	12	proficient	proficient	ADJ
app01-9397	8	13	model	model	NOUN
app01-9397	8	14	,	,	PUNCT
app01-9397	8	15	achieving	achieve	VERB
app01-9397	8	16	a	a	DET
app01-9397	8	17	mean	mean	ADJ
app01-9397	8	18	absolute	absolute	ADJ
app01-9397	8	19	percentage	percentage	NOUN
app01-9397	8	20	error	error	NOUN
app01-9397	8	21	of	of	ADP
app01-9397	8	22	0.367	0.367	NUM
app01-9397	8	23	±	±	NUM
app01-9397	8	24	0.230	0.230	NUM
app01-9397	8	25	.	.	PUNCT
app01-9397	9	1	keywords	keyword	NOUN
app01-9397	9	2	:	:	PUNCT
app01-9397	9	3	auxetic	auxetic	ADJ
app01-9397	9	4	structures	structure	NOUN
app01-9397	9	5	,	,	PUNCT
app01-9397	9	6	regression	regression	NOUN
app01-9397	9	7	,	,	PUNCT
app01-9397	9	8	machine	machine	NOUN
app01-9397	9	9	learning	learning	NOUN
app01-9397	9	10	.	.	PUNCT
app01-9397	10	1	1	1	X
app01-9397	10	2	.	.	X
app01-9397	10	3	introduction	introduction	NOUN
app01-9397	10	4	as	as	ADP
app01-9397	10	5	a	a	DET
app01-9397	10	6	main	main	ADJ
app01-9397	10	7	characteristic	characteristic	ADJ
app01-9397	10	8	auxetic	auxetic	ADJ
app01-9397	10	9	structures	structure	NOUN
app01-9397	10	10	posses	posse	NOUN
app01-9397	10	11	a	a	DET
app01-9397	10	12	negative	negative	ADJ
app01-9397	10	13	poisson	poisson	NOUN
app01-9397	10	14	’s	’s	PART
app01-9397	10	15	ratio	ratio	NOUN
app01-9397	10	16	,	,	PUNCT
app01-9397	10	17	which	which	PRON
app01-9397	10	18	makes	make	VERB
app01-9397	10	19	them	they	PRON
app01-9397	10	20	a	a	DET
app01-9397	10	21	subclass	subclass	NOUN
app01-9397	10	22	of	of	ADP
app01-9397	10	23	so	so	ADV
app01-9397	10	24	called	call	VERB
app01-9397	10	25	mechanical	mechanical	ADJ
app01-9397	10	26	metamaterials	metamaterial	NOUN
app01-9397	10	27	.	.	PUNCT
app01-9397	11	1	they	they	PRON
app01-9397	11	2	show	show	VERB
app01-9397	11	3	remarkable	remarkable	ADJ
app01-9397	11	4	mechanical	mechanical	ADJ
app01-9397	11	5	properties	property	NOUN
app01-9397	11	6	rendering	render	VERB
app01-9397	11	7	them	they	PRON
app01-9397	11	8	particularly	particularly	ADV
app01-9397	11	9	useful	useful	ADJ
app01-9397	11	10	for	for	ADP
app01-9397	11	11	applications	application	NOUN
app01-9397	11	12	as	as	ADP
app01-9397	11	13	crash	crash	NOUN
app01-9397	11	14	absorbers	absorber	NOUN
app01-9397	11	15	.	.	PUNCT
app01-9397	12	1	this	this	DET
app01-9397	12	2	aptitude	aptitude	NOUN
app01-9397	12	3	stems	stem	VERB
app01-9397	12	4	from	from	ADP
app01-9397	12	5	their	their	PRON
app01-9397	12	6	high	high	ADJ
app01-9397	12	7	stiffness	stiffness	NOUN
app01-9397	12	8	and	and	CCONJ
app01-9397	12	9	low	low	ADJ
app01-9397	12	10	density	density	NOUN
app01-9397	12	11	combined	combine	VERB
app01-9397	12	12	with	with	ADP
app01-9397	12	13	a	a	DET
app01-9397	12	14	plateau	plateau	NOUN
app01-9397	12	15	stress	stress	NOUN
app01-9397	12	16	over	over	ADP
app01-9397	12	17	a	a	DET
app01-9397	12	18	large	large	ADJ
app01-9397	12	19	deformation	deformation	NOUN
app01-9397	12	20	regime	regime	NOUN
app01-9397	12	21	[	[	X
app01-9397	12	22	1	1	NUM
app01-9397	12	23	,	,	PUNCT
app01-9397	12	24	2	2	NUM
app01-9397	12	25	]	]	PUNCT
app01-9397	12	26	.	.	PUNCT
app01-9397	13	1	the	the	DET
app01-9397	13	2	mechanical	mechanical	ADJ
app01-9397	13	3	performance	performance	NOUN
app01-9397	13	4	of	of	ADP
app01-9397	13	5	auxetic	auxetic	ADJ
app01-9397	13	6	structures	structure	NOUN
app01-9397	13	7	is	be	AUX
app01-9397	13	8	mainly	mainly	ADV
app01-9397	13	9	governed	govern	VERB
app01-9397	13	10	by	by	ADP
app01-9397	13	11	their	their	PRON
app01-9397	13	12	geometry	geometry	NOUN
app01-9397	13	13	making	make	VERB
app01-9397	13	14	structural	structural	ADJ
app01-9397	13	15	optimization	optimization	NOUN
app01-9397	13	16	a	a	DET
app01-9397	13	17	study	study	NOUN
app01-9397	13	18	topic	topic	NOUN
app01-9397	13	19	of	of	ADP
app01-9397	13	20	paramount	paramount	ADJ
app01-9397	13	21	importance	importance	NOUN
app01-9397	13	22	,	,	PUNCT
app01-9397	13	23	which	which	PRON
app01-9397	13	24	is	be	AUX
app01-9397	13	25	typically	typically	ADV
app01-9397	13	26	facilitated	facilitate	VERB
app01-9397	13	27	through	through	ADP
app01-9397	13	28	finite	finite	ADJ
app01-9397	13	29	element	element	NOUN
app01-9397	13	30	simulations	simulation	NOUN
app01-9397	13	31	.	.	PUNCT
app01-9397	14	1	since	since	SCONJ
app01-9397	14	2	those	those	PRON
app01-9397	14	3	can	can	AUX
app01-9397	14	4	be	be	AUX
app01-9397	14	5	very	very	ADV
app01-9397	14	6	time	time	NOUN
app01-9397	14	7	consuming	consume	VERB
app01-9397	14	8	given	give	VERB
app01-9397	14	9	the	the	DET
app01-9397	14	10	structural	structural	ADJ
app01-9397	14	11	complexity	complexity	NOUN
app01-9397	14	12	this	this	DET
app01-9397	14	13	work	work	NOUN
app01-9397	14	14	aims	aim	VERB
app01-9397	14	15	at	at	ADP
app01-9397	14	16	utilizing	utilize	VERB
app01-9397	14	17	a	a	DET
app01-9397	14	18	machine	machine	NOUN
app01-9397	14	19	learning	learn	VERB
app01-9397	14	20	approach	approach	NOUN
app01-9397	14	21	to	to	PART
app01-9397	14	22	reduce	reduce	VERB
app01-9397	14	23	the	the	DET
app01-9397	14	24	need	need	NOUN
app01-9397	14	25	for	for	ADP
app01-9397	14	26	lengthy	lengthy	ADJ
app01-9397	14	27	simulations	simulation	NOUN
app01-9397	14	28	–	–	PUNCT
app01-9397	14	29	a	a	DET
app01-9397	14	30	strategy	strategy	NOUN
app01-9397	14	31	commonly	commonly	ADV
app01-9397	14	32	pursued	pursue	VERB
app01-9397	14	33	in	in	ADP
app01-9397	14	34	contemporary	contemporary	ADJ
app01-9397	14	35	engineering	engineering	NOUN
app01-9397	14	36	sciences	science	NOUN
app01-9397	14	37	[	[	X
app01-9397	14	38	3	3	NUM
app01-9397	14	39	,	,	PUNCT
app01-9397	14	40	4	4	NUM
app01-9397	14	41	]	]	PUNCT
app01-9397	14	42	.	.	PUNCT
app01-9397	15	1	to	to	ADP
app01-9397	15	2	this	this	DET
app01-9397	15	3	end	end	NOUN
app01-9397	15	4	a	a	DET
app01-9397	15	5	multitude	multitude	NOUN
app01-9397	15	6	of	of	ADP
app01-9397	15	7	machine	machine	NOUN
app01-9397	15	8	learning	learn	VERB
app01-9397	15	9	algorithms	algorithm	NOUN
app01-9397	15	10	and	and	CCONJ
app01-9397	15	11	techniques	technique	NOUN
app01-9397	15	12	are	be	AUX
app01-9397	15	13	implemented	implement	VERB
app01-9397	15	14	and	and	CCONJ
app01-9397	15	15	compared	compare	VERB
app01-9397	15	16	regarding	regard	VERB
app01-9397	15	17	their	their	PRON
app01-9397	15	18	predictive	predictive	ADJ
app01-9397	15	19	performance	performance	NOUN
app01-9397	15	20	.	.	PUNCT
app01-9397	16	1	2	2	X
app01-9397	16	2	.	.	X
app01-9397	16	3	materials	material	NOUN
app01-9397	16	4	and	and	CCONJ
app01-9397	16	5	methods	method	NOUN
app01-9397	16	6	2.1	2.1	NUM
app01-9397	16	7	.	.	PUNCT
app01-9397	17	1	auxetic	auxetic	ADJ
app01-9397	17	2	structure	structure	NOUN
app01-9397	17	3	and	and	CCONJ
app01-9397	17	4	training	training	NOUN
app01-9397	17	5	data	datum	NOUN
app01-9397	17	6	the	the	DET
app01-9397	17	7	starting	starting	NOUN
app01-9397	17	8	point	point	NOUN
app01-9397	17	9	for	for	ADP
app01-9397	17	10	this	this	DET
app01-9397	17	11	study	study	NOUN
app01-9397	17	12	is	be	AUX
app01-9397	17	13	a	a	DET
app01-9397	17	14	specific	specific	ADJ
app01-9397	17	15	re	re	ADJ
app01-9397	17	16	-	-	ADJ
app01-9397	17	17	entrant	entrant	ADJ
app01-9397	17	18	auxetic	auxetic	ADJ
app01-9397	17	19	structure	structure	NOUN
app01-9397	17	20	previously	previously	ADV
app01-9397	17	21	studied	study	VERB
app01-9397	17	22	by	by	ADP
app01-9397	17	23	bronder	bronder	NOUN
app01-9397	17	24	et	et	PROPN
app01-9397	17	25	.	.	PUNCT
app01-9397	18	1	al	al	PROPN
app01-9397	18	2	.	.	PUNCT
app01-9397	19	1	[	[	X
app01-9397	19	2	5	5	NUM
app01-9397	19	3	]	]	PUNCT
app01-9397	19	4	.	.	PUNCT
app01-9397	20	1	the	the	DET
app01-9397	20	2	structure	structure	NOUN
app01-9397	20	3	is	be	AUX
app01-9397	20	4	shown	show	VERB
app01-9397	20	5	in	in	ADP
app01-9397	20	6	figure	figure	NOUN
app01-9397	20	7	1	1	NUM
app01-9397	20	8	and	and	CCONJ
app01-9397	20	9	is	be	AUX
app01-9397	20	10	originally	originally	ADV
app01-9397	20	11	defined	define	VERB
app01-9397	20	12	by	by	ADP
app01-9397	20	13	five	five	NUM
app01-9397	20	14	geometry	geometry	NOUN
app01-9397	20	15	parameters	parameter	NOUN
app01-9397	20	16	:	:	PUNCT
app01-9397	20	17	length	length	NOUN
app01-9397	20	18	,	,	PUNCT
app01-9397	20	19	size	size	NOUN
app01-9397	20	20	,	,	PUNCT
app01-9397	20	21	waist	waist	NOUN
app01-9397	20	22	,	,	PUNCT
app01-9397	20	23	strut	strut	NOUN
app01-9397	20	24	thickness	thickness	NOUN
app01-9397	20	25	and	and	CCONJ
app01-9397	20	26	angle	angle	NOUN
app01-9397	20	27	.	.	PUNCT
app01-9397	21	1	as	as	ADP
app01-9397	21	2	part	part	NOUN
app01-9397	21	3	of	of	ADP
app01-9397	21	4	a	a	DET
app01-9397	21	5	carried	carry	VERB
app01-9397	21	6	out	out	ADP
app01-9397	21	7	feature	feature	NOUN
app01-9397	21	8	engineering	engineering	NOUN
app01-9397	21	9	process	process	NOUN
app01-9397	21	10	an	an	DET
app01-9397	21	11	addition	addition	NOUN
app01-9397	21	12	geometry	geometry	NOUN
app01-9397	21	13	parameter	parameter	NOUN
app01-9397	21	14	named	name	VERB
app01-9397	21	15	gap	gap	NOUN
app01-9397	21	16	is	be	AUX
app01-9397	21	17	introduced	introduce	VERB
app01-9397	21	18	,	,	PUNCT
app01-9397	21	19	which	which	PRON
app01-9397	21	20	is	be	AUX
app01-9397	21	21	derived	derive	VERB
app01-9397	21	22	from	from	ADP
app01-9397	21	23	the	the	DET
app01-9397	21	24	original	original	ADJ
app01-9397	21	25	features	feature	NOUN
app01-9397	21	26	according	accord	VERB
app01-9397	21	27	to	to	ADP
app01-9397	21	28	gap	gap	NOUN
app01-9397	21	29	=	=	PUNCT
app01-9397	22	1	[	[	X
app01-9397	22	2	1	1	NUM
app01-9397	22	3	−	−	PROPN
app01-9397	22	4	tan	tan	PROPN
app01-9397	22	5	(	(	PUNCT
app01-9397	22	6	90	90	NUM
app01-9397	22	7	◦	◦	NOUN
app01-9397	22	8	−	−	PROPN
app01-9397	22	9	angle	angle	NOUN
app01-9397	22	10	)	)	PUNCT
app01-9397	22	11	]	]	PUNCT
app01-9397	22	12	size	size	NOUN
app01-9397	22	13	−	−	PROPN
app01-9397	22	14	length	length	NOUN
app01-9397	22	15	.	.	PUNCT
app01-9397	23	1	(	(	PUNCT
app01-9397	23	2	1	1	X
app01-9397	23	3	)	)	PUNCT
app01-9397	23	4	figure	figure	NOUN
app01-9397	23	5	1	1	NUM
app01-9397	23	6	.	.	PUNCT
app01-9397	23	7	auxetic	auxetic	ADJ
app01-9397	23	8	unit	unit	NOUN
app01-9397	23	9	cell	cell	NOUN
app01-9397	23	10	with	with	ADP
app01-9397	23	11	five	five	NUM
app01-9397	23	12	defined	define	VERB
app01-9397	23	13	geometry	geometry	NOUN
app01-9397	23	14	parameters	parameter	NOUN
app01-9397	23	15	(	(	PUNCT
app01-9397	23	16	red	red	PROPN
app01-9397	23	17	)	)	PUNCT
app01-9397	23	18	,	,	PUNCT
app01-9397	23	19	which	which	PRON
app01-9397	23	20	represent	represent	VERB
app01-9397	23	21	the	the	DET
app01-9397	23	22	initial	initial	ADJ
app01-9397	23	23	feature	feature	NOUN
app01-9397	23	24	space	space	NOUN
app01-9397	23	25	for	for	ADP
app01-9397	23	26	the	the	DET
app01-9397	23	27	modeling	modeling	NOUN
app01-9397	23	28	task	task	NOUN
app01-9397	23	29	at	at	ADP
app01-9397	23	30	hand	hand	NOUN
app01-9397	23	31	.	.	PUNCT
app01-9397	24	1	an	an	DET
app01-9397	24	2	addition	addition	NOUN
app01-9397	24	3	parameter	parameter	NOUN
app01-9397	24	4	was	be	AUX
app01-9397	24	5	derived	derive	VERB
app01-9397	24	6	during	during	ADP
app01-9397	24	7	feature	feature	NOUN
app01-9397	24	8	engineering	engineering	NOUN
app01-9397	24	9	(	(	PUNCT
app01-9397	24	10	blue	blue	ADJ
app01-9397	24	11	)	)	PUNCT
app01-9397	24	12	.	.	PUNCT
app01-9397	25	1	from	from	ADP
app01-9397	25	2	bronder	bronder	PROPN
app01-9397	25	3	et	et	PROPN
app01-9397	25	4	.	.	PUNCT
app01-9397	26	1	al	al	PROPN
app01-9397	26	2	.	.	PUNCT
app01-9397	27	1	[	[	X
app01-9397	27	2	5	5	NUM
app01-9397	27	3	]	]	PUNCT
app01-9397	27	4	,	,	PUNCT
app01-9397	27	5	modified	modify	VERB
app01-9397	27	6	.	.	PUNCT
app01-9397	28	1	for	for	ADP
app01-9397	28	2	this	this	DET
app01-9397	28	3	project	project	NOUN
app01-9397	28	4	a	a	DET
app01-9397	28	5	set	set	NOUN
app01-9397	28	6	of	of	ADP
app01-9397	28	7	130	130	NUM
app01-9397	28	8	stress	stress	NOUN
app01-9397	28	9	-	-	PUNCT
app01-9397	28	10	strain	strain	NOUN
app01-9397	28	11	curves	curve	NOUN
app01-9397	28	12	was	be	AUX
app01-9397	28	13	available	available	ADJ
app01-9397	28	14	,	,	PUNCT
app01-9397	28	15	which	which	PRON
app01-9397	28	16	can	can	AUX
app01-9397	28	17	be	be	AUX
app01-9397	28	18	seen	see	VERB
app01-9397	28	19	in	in	ADP
app01-9397	28	20	figure	figure	NOUN
app01-9397	28	21	2	2	NUM
app01-9397	28	22	.	.	PUNCT
app01-9397	29	1	the	the	DET
app01-9397	29	2	term	term	NOUN
app01-9397	29	3	stress	stress	NOUN
app01-9397	29	4	,	,	PUNCT
app01-9397	29	5	in	in	ADP
app01-9397	29	6	the	the	DET
app01-9397	29	7	context	context	NOUN
app01-9397	29	8	of	of	ADP
app01-9397	29	9	this	this	DET
app01-9397	29	10	paper	paper	NOUN
app01-9397	29	11	,	,	PUNCT
app01-9397	29	12	always	always	ADV
app01-9397	29	13	refers	refer	VERB
app01-9397	29	14	to	to	ADP
app01-9397	29	15	the	the	DET
app01-9397	29	16	mass	mass	ADV
app01-9397	29	17	-	-	PUNCT
app01-9397	29	18	normalized	normalize	VERB
app01-9397	29	19	stress	stress	NOUN
app01-9397	29	20	in	in	ADP
app01-9397	29	21	mpa	mpa	PROPN
app01-9397	29	22	g−1	g−1	PROPN
app01-9397	29	23	.	.	PROPN
app01-9397	30	1	from	from	ADP
app01-9397	30	2	all	all	DET
app01-9397	30	3	available	available	ADJ
app01-9397	30	4	curves	curve	NOUN
app01-9397	30	5	,	,	PUNCT
app01-9397	30	6	15	15	NUM
app01-9397	30	7	were	be	AUX
app01-9397	30	8	reserved	reserve	VERB
app01-9397	30	9	for	for	ADP
app01-9397	30	10	the	the	DET
app01-9397	30	11	final	final	ADJ
app01-9397	30	12	performance	performance	NOUN
app01-9397	30	13	evaluation	evaluation	NOUN
app01-9397	30	14	at	at	ADP
app01-9397	30	15	the	the	DET
app01-9397	30	16	very	very	ADJ
app01-9397	30	17	end	end	NOUN
app01-9397	30	18	of	of	ADP
app01-9397	30	19	the	the	DET
app01-9397	30	20	project	project	NOUN
app01-9397	30	21	,	,	PUNCT
app01-9397	30	22	leaving	leave	VERB
app01-9397	30	23	115	115	NUM
app01-9397	30	24	example	example	NOUN
app01-9397	30	25	curves	curve	NOUN
app01-9397	30	26	for	for	ADP
app01-9397	30	27	model	model	NOUN
app01-9397	30	28	development	development	NOUN
app01-9397	30	29	.	.	PUNCT
app01-9397	31	1	the	the	DET
app01-9397	31	2	130	130	NUM
app01-9397	31	3	curves	curve	NOUN
app01-9397	31	4	were	be	AUX
app01-9397	31	5	produced	produce	VERB
app01-9397	31	6	by	by	ADP
app01-9397	31	7	validated	validate	VERB
app01-9397	31	8	finite	finite	ADJ
app01-9397	31	9	element	element	NOUN
app01-9397	31	10	simulations	simulation	NOUN
app01-9397	31	11	from	from	ADP
app01-9397	31	12	the	the	DET
app01-9397	31	13	software	software	NOUN
app01-9397	31	14	abaqus	abaqus	PROPN
app01-9397	31	15	®	®	PROPN
app01-9397	31	16	.	.	PUNCT
app01-9397	32	1	alsi10	alsi10	PROPN
app01-9397	32	2	mg	mg	PROPN
app01-9397	32	3	structures	structure	NOUN
app01-9397	32	4	of	of	ADP
app01-9397	32	5	3	3	NUM
app01-9397	32	6	×	×	NOUN
app01-9397	32	7	3	3	NUM
app01-9397	32	8	×	×	NOUN
app01-9397	32	9	3	3	NUM
app01-9397	32	10	unit	unit	NOUN
app01-9397	32	11	cells	cell	NOUN
app01-9397	32	12	were	be	AUX
app01-9397	32	13	simulated	simulate	VERB
app01-9397	32	14	using	use	VERB
app01-9397	32	15	an	an	DET
app01-9397	32	16	explicit	explicit	ADJ
app01-9397	32	17	solver	solver	NOUN
app01-9397	32	18	.	.	PUNCT
app01-9397	33	1	32	32	NUM
app01-9397	33	2	https://doi.org/10.14311/app.2023.42.0032	https://doi.org/10.14311/app.2023.42.0032	PROPN
app01-9397	33	3	https://creativecommons.org/licenses/by/4.0/	https://creativecommons.org/licenses/by/4.0/	PROPN
app01-9397	33	4	https://www.cvut.cz/en	https://www.cvut.cz/en	NOUN
app01-9397	33	5	vol	vol	NOUN
app01-9397	33	6	.	.	PUNCT
app01-9397	34	1	42/2023	42/2023	NUM
app01-9397	34	2	ai	ai	AUX
app01-9397	34	3	-	-	PUNCT
app01-9397	34	4	assisted	assist	VERB
app01-9397	34	5	study	study	NOUN
app01-9397	34	6	of	of	ADP
app01-9397	34	7	auxetic	auxetic	ADJ
app01-9397	34	8	structures	structure	NOUN
app01-9397	34	9	figure	figure	VERB
app01-9397	34	10	2	2	NUM
app01-9397	34	11	.	.	PUNCT
app01-9397	35	1	all	all	DET
app01-9397	35	2	130	130	NUM
app01-9397	35	3	stress	stress	NOUN
app01-9397	35	4	-	-	PUNCT
app01-9397	35	5	strain	strain	NOUN
app01-9397	35	6	curves	curve	NOUN
app01-9397	35	7	available	available	ADJ
app01-9397	35	8	for	for	ADP
app01-9397	35	9	the	the	DET
app01-9397	35	10	project	project	NOUN
app01-9397	35	11	.	.	PUNCT
app01-9397	36	1	with	with	ADP
app01-9397	36	2	lightweight	lightweight	ADJ
app01-9397	36	3	applications	application	NOUN
app01-9397	36	4	in	in	ADP
app01-9397	36	5	mind	mind	NOUN
app01-9397	36	6	,	,	PUNCT
app01-9397	36	7	stress	stress	NOUN
app01-9397	36	8	values	value	NOUN
app01-9397	36	9	were	be	AUX
app01-9397	36	10	normalized	normalize	VERB
app01-9397	36	11	with	with	ADP
app01-9397	36	12	regard	regard	NOUN
app01-9397	36	13	to	to	ADP
app01-9397	36	14	the	the	DET
app01-9397	36	15	structure	structure	NOUN
app01-9397	36	16	’s	’s	PART
app01-9397	36	17	weight	weight	NOUN
app01-9397	36	18	.	.	PUNCT
app01-9397	37	1	for	for	ADP
app01-9397	37	2	simplicity	simplicity	NOUN
app01-9397	37	3	reasons	reason	NOUN
app01-9397	37	4	compressive	compressive	ADJ
app01-9397	37	5	strain	strain	NOUN
app01-9397	37	6	values	value	NOUN
app01-9397	37	7	are	be	AUX
app01-9397	37	8	positive	positive	ADJ
app01-9397	37	9	.	.	PUNCT
app01-9397	38	1	2.2	2.2	NUM
app01-9397	38	2	.	.	PUNCT
app01-9397	38	3	error	error	NOUN
app01-9397	38	4	metric	metric	NOUN
app01-9397	38	5	the	the	DET
app01-9397	38	6	problem	problem	NOUN
app01-9397	38	7	at	at	ADP
app01-9397	38	8	hand	hand	NOUN
app01-9397	38	9	is	be	AUX
app01-9397	38	10	a	a	DET
app01-9397	38	11	supervised	supervised	ADJ
app01-9397	38	12	regression	regression	NOUN
app01-9397	38	13	problem	problem	NOUN
app01-9397	38	14	where	where	SCONJ
app01-9397	38	15	a	a	DET
app01-9397	38	16	model	model	NOUN
app01-9397	38	17	predicts	predict	VERB
app01-9397	38	18	numerical	numerical	ADJ
app01-9397	38	19	values	value	NOUN
app01-9397	38	20	,	,	PUNCT
app01-9397	38	21	in	in	ADP
app01-9397	38	22	this	this	DET
app01-9397	38	23	case	case	NOUN
app01-9397	38	24	stresses	stress	VERB
app01-9397	38	25	,	,	PUNCT
app01-9397	38	26	while	while	SCONJ
app01-9397	38	27	minimizing	minimize	VERB
app01-9397	38	28	the	the	DET
app01-9397	38	29	prediction	prediction	NOUN
app01-9397	38	30	error	error	NOUN
app01-9397	38	31	.	.	PUNCT
app01-9397	39	1	due	due	ADP
app01-9397	39	2	to	to	ADP
app01-9397	39	3	the	the	DET
app01-9397	39	4	curves	curve	NOUN
app01-9397	39	5	potentially	potentially	ADV
app01-9397	39	6	spreading	spread	VERB
app01-9397	39	7	over	over	ADP
app01-9397	39	8	multiple	multiple	ADJ
app01-9397	39	9	orders	order	NOUN
app01-9397	39	10	of	of	ADP
app01-9397	39	11	magnitude	magnitude	NOUN
app01-9397	39	12	,	,	PUNCT
app01-9397	39	13	the	the	DET
app01-9397	39	14	scale	scale	NOUN
app01-9397	39	15	-	-	PUNCT
app01-9397	39	16	independent	independent	ADJ
app01-9397	39	17	mean	mean	ADJ
app01-9397	39	18	absolute	absolute	ADJ
app01-9397	39	19	percentage	percentage	NOUN
app01-9397	39	20	error	error	NOUN
app01-9397	39	21	(	(	PUNCT
app01-9397	39	22	mape	mape	NOUN
app01-9397	39	23	)	)	PUNCT
app01-9397	39	24	is	be	AUX
app01-9397	39	25	chosen	choose	VERB
app01-9397	39	26	as	as	ADP
app01-9397	39	27	the	the	DET
app01-9397	39	28	error	error	NOUN
app01-9397	39	29	metric	metric	NOUN
app01-9397	39	30	,	,	PUNCT
app01-9397	39	31	which	which	PRON
app01-9397	39	32	is	be	AUX
app01-9397	39	33	defined	define	VERB
app01-9397	39	34	as	as	ADP
app01-9397	39	35	mape	mape	NOUN
app01-9397	39	36	=	=	SYM
app01-9397	39	37	1	1	NUM
app01-9397	39	38	n	n	NUM
app01-9397	39	39	n∑	n∑	NOUN
app01-9397	40	1	i	i	PRON
app01-9397	40	2	∣∣∣∣yi	∣∣∣∣yi	VERB
app01-9397	40	3	−	−	PROPN
app01-9397	40	4	ŷi	ŷi	PROPN
app01-9397	40	5	yi	yi	PROPN
app01-9397	40	6	∣∣∣∣	∣∣∣∣	NOUN
app01-9397	40	7	,	,	PUNCT
app01-9397	40	8	(	(	PUNCT
app01-9397	40	9	2	2	X
app01-9397	40	10	)	)	PUNCT
app01-9397	40	11	where	where	SCONJ
app01-9397	40	12	n	n	PRON
app01-9397	40	13	is	be	AUX
app01-9397	40	14	the	the	DET
app01-9397	40	15	number	number	NOUN
app01-9397	40	16	of	of	ADP
app01-9397	40	17	predictions	prediction	NOUN
app01-9397	40	18	,	,	PUNCT
app01-9397	40	19	ŷi	ŷi	PROPN
app01-9397	40	20	the	the	DET
app01-9397	40	21	i	i	PROPN
app01-9397	40	22	-	-	PUNCT
app01-9397	40	23	th	th	X
app01-9397	40	24	predicted	predict	VERB
app01-9397	40	25	value	value	NOUN
app01-9397	40	26	and	and	CCONJ
app01-9397	40	27	yi	yi	VERB
app01-9397	40	28	the	the	DET
app01-9397	40	29	corresponding	correspond	VERB
app01-9397	40	30	i	i	PROPN
app01-9397	40	31	-	-	PUNCT
app01-9397	40	32	th	th	X
app01-9397	40	33	true	true	ADJ
app01-9397	40	34	value	value	NOUN
app01-9397	40	35	.	.	PUNCT
app01-9397	41	1	since	since	SCONJ
app01-9397	41	2	the	the	DET
app01-9397	41	3	mape	mape	NOUN
app01-9397	41	4	becomes	become	VERB
app01-9397	41	5	arbitrarily	arbitrarily	ADV
app01-9397	41	6	large	large	ADJ
app01-9397	41	7	for	for	ADP
app01-9397	41	8	true	true	ADJ
app01-9397	41	9	target	target	NOUN
app01-9397	41	10	values	value	NOUN
app01-9397	41	11	,	,	PUNCT
app01-9397	41	12	i.e.	i.e.	X
app01-9397	41	13	the	the	DET
app01-9397	41	14	variable	variable	ADJ
app01-9397	41	15	values	value	NOUN
app01-9397	41	16	to	to	PART
app01-9397	41	17	be	be	AUX
app01-9397	41	18	predicted	predict	VERB
app01-9397	41	19	,	,	PUNCT
app01-9397	41	20	that	that	PRON
app01-9397	41	21	are	be	AUX
app01-9397	41	22	zero	zero	NUM
app01-9397	41	23	or	or	CCONJ
app01-9397	41	24	close	close	ADJ
app01-9397	41	25	to	to	ADP
app01-9397	41	26	zero	zero	NUM
app01-9397	41	27	,	,	PUNCT
app01-9397	41	28	the	the	DET
app01-9397	41	29	metric	metric	NOUN
app01-9397	41	30	is	be	AUX
app01-9397	41	31	evaluated	evaluate	VERB
app01-9397	41	32	only	only	ADV
app01-9397	41	33	for	for	ADP
app01-9397	41	34	strains	strain	NOUN
app01-9397	41	35	>	>	X
app01-9397	41	36	0.01	0.01	NUM
app01-9397	41	37	,	,	PUNCT
app01-9397	41	38	which	which	PRON
app01-9397	41	39	corresponds	correspond	VERB
app01-9397	41	40	to	to	ADP
app01-9397	41	41	the	the	DET
app01-9397	41	42	initial	initial	ADJ
app01-9397	41	43	linear	linear	ADJ
app01-9397	41	44	elastic	elastic	ADJ
app01-9397	41	45	part	part	NOUN
app01-9397	41	46	of	of	ADP
app01-9397	41	47	the	the	DET
app01-9397	41	48	curves	curve	NOUN
app01-9397	41	49	.	.	PUNCT
app01-9397	42	1	2.3	2.3	NUM
app01-9397	42	2	.	.	PUNCT
app01-9397	43	1	problem	problem	NOUN
app01-9397	43	2	formulation	formulation	NOUN
app01-9397	43	3	for	for	ADP
app01-9397	43	4	the	the	DET
app01-9397	43	5	main	main	ADJ
app01-9397	43	6	study	study	NOUN
app01-9397	43	7	two	two	NUM
app01-9397	43	8	different	different	ADJ
app01-9397	43	9	approaches	approach	NOUN
app01-9397	43	10	to	to	ADP
app01-9397	43	11	the	the	DET
app01-9397	43	12	problem	problem	NOUN
app01-9397	43	13	itself	itself	PRON
app01-9397	43	14	are	be	AUX
app01-9397	43	15	studied	study	VERB
app01-9397	43	16	.	.	PUNCT
app01-9397	44	1	in	in	ADP
app01-9397	44	2	the	the	DET
app01-9397	44	3	single	single	ADJ
app01-9397	44	4	point	point	NOUN
app01-9397	44	5	approach	approach	NOUN
app01-9397	44	6	(	(	PUNCT
app01-9397	44	7	sp	sp	NOUN
app01-9397	44	8	)	)	PUNCT
app01-9397	44	9	the	the	DET
app01-9397	44	10	models	model	NOUN
app01-9397	44	11	take	take	VERB
app01-9397	44	12	as	as	SCONJ
app01-9397	44	13	input	input	NOUN
app01-9397	44	14	the	the	DET
app01-9397	44	15	geometry	geometry	NOUN
app01-9397	44	16	parameters	parameter	NOUN
app01-9397	44	17	describing	describe	VERB
app01-9397	44	18	the	the	DET
app01-9397	44	19	structure	structure	NOUN
app01-9397	44	20	as	as	ADV
app01-9397	44	21	well	well	ADV
app01-9397	44	22	as	as	ADP
app01-9397	44	23	a	a	DET
app01-9397	44	24	certain	certain	ADJ
app01-9397	44	25	strain	strain	NOUN
app01-9397	44	26	value	value	NOUN
app01-9397	44	27	and	and	CCONJ
app01-9397	44	28	predict	predict	VERB
app01-9397	44	29	the	the	DET
app01-9397	44	30	corresponding	correspond	VERB
app01-9397	44	31	mass	mass	NOUN
app01-9397	44	32	normalized	normalize	VERB
app01-9397	44	33	stress	stress	NOUN
app01-9397	44	34	value	value	NOUN
app01-9397	44	35	.	.	PUNCT
app01-9397	45	1	in	in	ADP
app01-9397	45	2	contrast	contrast	NOUN
app01-9397	45	3	,	,	PUNCT
app01-9397	45	4	using	use	VERB
app01-9397	45	5	the	the	DET
app01-9397	45	6	whole	whole	ADJ
app01-9397	45	7	curve	curve	NOUN
app01-9397	45	8	approach	approach	NOUN
app01-9397	45	9	(	(	PUNCT
app01-9397	45	10	wc	wc	PROPN
app01-9397	45	11	)	)	PUNCT
app01-9397	45	12	the	the	DET
app01-9397	45	13	model	model	NOUN
app01-9397	45	14	predicts	predict	VERB
app01-9397	45	15	a	a	DET
app01-9397	45	16	complete	complete	ADJ
app01-9397	45	17	sub	sub	ADJ
app01-9397	45	18	-	-	ADJ
app01-9397	45	19	sampled	sample	VERB
app01-9397	45	20	curve	curve	NOUN
app01-9397	45	21	based	base	VERB
app01-9397	45	22	on	on	ADP
app01-9397	45	23	only	only	ADV
app01-9397	45	24	the	the	DET
app01-9397	45	25	geometry	geometry	NOUN
app01-9397	45	26	parameters	parameter	NOUN
app01-9397	45	27	.	.	PUNCT
app01-9397	46	1	here	here	ADV
app01-9397	46	2	,	,	PUNCT
app01-9397	46	3	a	a	DET
app01-9397	46	4	sampling	sample	VERB
app01-9397	46	5	rate	rate	NOUN
app01-9397	46	6	of	of	ADP
app01-9397	46	7	20	20	NUM
app01-9397	46	8	is	be	AUX
app01-9397	46	9	used	use	VERB
app01-9397	46	10	,	,	PUNCT
app01-9397	46	11	reducing	reduce	VERB
app01-9397	46	12	the	the	DET
app01-9397	46	13	number	number	NOUN
app01-9397	46	14	of	of	ADP
app01-9397	46	15	data	datum	NOUN
app01-9397	46	16	points	point	NOUN
app01-9397	46	17	per	per	ADP
app01-9397	46	18	curve	curve	NOUN
app01-9397	46	19	from	from	ADP
app01-9397	46	20	1	1	NUM
app01-9397	46	21	001	001	NUM
app01-9397	46	22	to	to	ADP
app01-9397	46	23	51	51	NUM
app01-9397	46	24	.	.	PUNCT
app01-9397	47	1	during	during	ADP
app01-9397	47	2	the	the	DET
app01-9397	47	3	exploratory	exploratory	ADJ
app01-9397	47	4	phase	phase	NOUN
app01-9397	47	5	of	of	ADP
app01-9397	47	6	the	the	DET
app01-9397	47	7	study	study	NOUN
app01-9397	47	8	different	different	ADJ
app01-9397	47	9	sampling	sampling	NOUN
app01-9397	47	10	rates	rate	NOUN
app01-9397	47	11	were	be	AUX
app01-9397	47	12	tested	test	VERB
app01-9397	47	13	with	with	ADP
app01-9397	47	14	20	20	NUM
app01-9397	47	15	leading	lead	VERB
app01-9397	47	16	to	to	ADP
app01-9397	47	17	the	the	DET
app01-9397	47	18	best	good	ADJ
app01-9397	47	19	results	result	NOUN
app01-9397	47	20	.	.	PUNCT
app01-9397	48	1	in	in	ADP
app01-9397	48	2	particular	particular	ADJ
app01-9397	48	3	,	,	PUNCT
app01-9397	48	4	better	well	ADJ
app01-9397	48	5	results	result	NOUN
app01-9397	48	6	and	and	CCONJ
app01-9397	48	7	faster	fast	ADJ
app01-9397	48	8	training	training	NOUN
app01-9397	48	9	times	time	NOUN
app01-9397	48	10	were	be	AUX
app01-9397	48	11	achieved	achieve	VERB
app01-9397	48	12	compared	compare	VERB
app01-9397	48	13	to	to	ADP
app01-9397	48	14	the	the	DET
app01-9397	48	15	original	original	ADJ
app01-9397	48	16	data	datum	NOUN
app01-9397	48	17	point	point	NOUN
app01-9397	48	18	density	density	NOUN
app01-9397	48	19	.	.	PUNCT
app01-9397	49	1	figure	figure	NOUN
app01-9397	49	2	3	3	NUM
app01-9397	49	3	.	.	PUNCT
app01-9397	49	4	topology	topology	NOUN
app01-9397	49	5	of	of	ADP
app01-9397	49	6	a	a	DET
app01-9397	49	7	feed	feed	NOUN
app01-9397	49	8	-	-	PUNCT
app01-9397	49	9	forward	forward	ADV
app01-9397	49	10	neural	neural	ADJ
app01-9397	49	11	network	network	NOUN
app01-9397	49	12	.	.	PUNCT
app01-9397	50	1	neurons	neuron	NOUN
app01-9397	50	2	are	be	AUX
app01-9397	50	3	represented	represent	VERB
app01-9397	50	4	by	by	ADP
app01-9397	50	5	circles	circle	NOUN
app01-9397	50	6	,	,	PUNCT
app01-9397	50	7	while	while	SCONJ
app01-9397	50	8	arrow	arrow	NOUN
app01-9397	50	9	indicate	indicate	VERB
app01-9397	50	10	the	the	DET
app01-9397	50	11	direction	direction	NOUN
app01-9397	50	12	of	of	ADP
app01-9397	50	13	data	datum	NOUN
app01-9397	50	14	transfer	transfer	NOUN
app01-9397	50	15	.	.	PUNCT
app01-9397	51	1	to	to	PART
app01-9397	51	2	facilitate	facilitate	VERB
app01-9397	51	3	a	a	DET
app01-9397	51	4	direct	direct	ADJ
app01-9397	51	5	and	and	CCONJ
app01-9397	51	6	transparent	transparent	ADJ
app01-9397	51	7	comparison	comparison	NOUN
app01-9397	51	8	of	of	ADP
app01-9397	51	9	the	the	DET
app01-9397	51	10	results	result	NOUN
app01-9397	51	11	the	the	DET
app01-9397	51	12	predicted	predict	VERB
app01-9397	51	13	sub	sub	ADJ
app01-9397	51	14	-	-	ADJ
app01-9397	51	15	sampled	sample	VERB
app01-9397	51	16	curves	curve	NOUN
app01-9397	51	17	are	be	AUX
app01-9397	51	18	linearly	linearly	ADV
app01-9397	51	19	extrapolated	extrapolate	VERB
app01-9397	51	20	to	to	PART
app01-9397	51	21	have	have	VERB
app01-9397	51	22	the	the	DET
app01-9397	51	23	same	same	ADJ
app01-9397	51	24	point	point	NOUN
app01-9397	51	25	density	density	NOUN
app01-9397	51	26	as	as	ADP
app01-9397	51	27	the	the	DET
app01-9397	51	28	original	original	ADJ
app01-9397	51	29	curves	curve	NOUN
app01-9397	51	30	.	.	PUNCT
app01-9397	52	1	moreover	moreover	ADV
app01-9397	52	2	,	,	PUNCT
app01-9397	52	3	in	in	ADP
app01-9397	52	4	an	an	DET
app01-9397	52	5	additional	additional	ADJ
app01-9397	52	6	approach	approach	NOUN
app01-9397	52	7	,	,	PUNCT
app01-9397	52	8	the	the	DET
app01-9397	52	9	potential	potential	NOUN
app01-9397	52	10	of	of	ADP
app01-9397	52	11	treating	treat	VERB
app01-9397	52	12	the	the	DET
app01-9397	52	13	entire	entire	ADJ
app01-9397	52	14	curve	curve	NOUN
app01-9397	52	15	as	as	SCONJ
app01-9397	52	16	a	a	DET
app01-9397	52	17	sequence	sequence	NOUN
app01-9397	52	18	is	be	AUX
app01-9397	52	19	explored	explore	VERB
app01-9397	52	20	.	.	PUNCT
app01-9397	53	1	in	in	ADP
app01-9397	53	2	this	this	DET
app01-9397	53	3	sequential	sequential	ADJ
app01-9397	53	4	curve	curve	NOUN
app01-9397	53	5	approach	approach	NOUN
app01-9397	53	6	(	(	PUNCT
app01-9397	53	7	sc	sc	PROPN
app01-9397	53	8	)	)	PUNCT
app01-9397	53	9	,	,	PUNCT
app01-9397	53	10	the	the	DET
app01-9397	53	11	model	model	NOUN
app01-9397	53	12	is	be	AUX
app01-9397	53	13	tasked	task	VERB
app01-9397	53	14	with	with	ADP
app01-9397	53	15	predicting	predict	VERB
app01-9397	53	16	all	all	DET
app01-9397	53	17	1	1	NUM
app01-9397	53	18	001	001	NUM
app01-9397	53	19	points	point	NOUN
app01-9397	53	20	of	of	ADP
app01-9397	53	21	the	the	DET
app01-9397	53	22	stressstrain	stressstrain	NOUN
app01-9397	53	23	curve	curve	NOUN
app01-9397	53	24	based	base	VERB
app01-9397	53	25	on	on	ADP
app01-9397	53	26	the	the	DET
app01-9397	53	27	geometry	geometry	NOUN
app01-9397	53	28	parameters	parameter	NOUN
app01-9397	53	29	.	.	PUNCT
app01-9397	54	1	2.4	2.4	NUM
app01-9397	54	2	.	.	PUNCT
app01-9397	54	3	machine	machine	NOUN
app01-9397	54	4	learning	learning	NOUN
app01-9397	54	5	models	model	NOUN
app01-9397	54	6	four	four	NUM
app01-9397	54	7	fundamentally	fundamentally	ADV
app01-9397	54	8	different	different	ADJ
app01-9397	54	9	classes	class	NOUN
app01-9397	54	10	of	of	ADP
app01-9397	54	11	machine	machine	NOUN
app01-9397	54	12	learning	learning	NOUN
app01-9397	54	13	models	model	NOUN
app01-9397	54	14	are	be	AUX
app01-9397	54	15	used	use	VERB
app01-9397	54	16	to	to	PART
app01-9397	54	17	make	make	VERB
app01-9397	54	18	prediction	prediction	NOUN
app01-9397	54	19	in	in	ADP
app01-9397	54	20	order	order	NOUN
app01-9397	54	21	to	to	PART
app01-9397	54	22	provide	provide	VERB
app01-9397	54	23	an	an	DET
app01-9397	54	24	extensive	extensive	ADJ
app01-9397	54	25	overview	overview	NOUN
app01-9397	54	26	and	and	CCONJ
app01-9397	54	27	comparison	comparison	NOUN
app01-9397	54	28	of	of	ADP
app01-9397	54	29	methods	method	NOUN
app01-9397	54	30	potentially	potentially	ADV
app01-9397	54	31	suitable	suitable	ADJ
app01-9397	54	32	for	for	ADP
app01-9397	54	33	the	the	DET
app01-9397	54	34	task	task	NOUN
app01-9397	54	35	at	at	ADP
app01-9397	54	36	hand	hand	NOUN
app01-9397	54	37	.	.	PUNCT
app01-9397	55	1	representing	represent	VERB
app01-9397	55	2	the	the	DET
app01-9397	55	3	class	class	NOUN
app01-9397	55	4	of	of	ADP
app01-9397	55	5	parametric	parametric	ADJ
app01-9397	55	6	models	model	NOUN
app01-9397	55	7	,	,	PUNCT
app01-9397	55	8	i.e.	i.e.	X
app01-9397	55	9	models	model	NOUN
app01-9397	55	10	that	that	PRON
app01-9397	55	11	learn	learn	VERB
app01-9397	55	12	by	by	ADP
app01-9397	55	13	fitting	fitting	ADJ
app01-9397	55	14	parameters	parameter	NOUN
app01-9397	55	15	of	of	ADP
app01-9397	55	16	a	a	DET
app01-9397	55	17	mapping	mapping	NOUN
app01-9397	55	18	function	function	NOUN
app01-9397	55	19	to	to	ADP
app01-9397	55	20	the	the	DET
app01-9397	55	21	data	datum	NOUN
app01-9397	55	22	,	,	PUNCT
app01-9397	55	23	artificial	artificial	ADJ
app01-9397	55	24	neural	neural	ADJ
app01-9397	55	25	networks	network	NOUN
app01-9397	55	26	(	(	PUNCT
app01-9397	55	27	ann	ann	PROPN
app01-9397	55	28	)	)	PUNCT
app01-9397	55	29	and	and	CCONJ
app01-9397	55	30	support	support	VERB
app01-9397	55	31	vector	vector	NOUN
app01-9397	55	32	regression	regression	NOUN
app01-9397	55	33	(	(	PUNCT
app01-9397	55	34	svr	svr	PROPN
app01-9397	55	35	)	)	PUNCT
app01-9397	55	36	are	be	AUX
app01-9397	55	37	implemented	implement	VERB
app01-9397	55	38	.	.	PUNCT
app01-9397	56	1	anns	anns	PROPN
app01-9397	56	2	consist	consist	VERB
app01-9397	56	3	of	of	ADP
app01-9397	56	4	a	a	DET
app01-9397	56	5	multitude	multitude	NOUN
app01-9397	56	6	of	of	ADP
app01-9397	56	7	simple	simple	ADJ
app01-9397	56	8	computational	computational	ADJ
app01-9397	56	9	units	unit	NOUN
app01-9397	56	10	,	,	PUNCT
app01-9397	56	11	called	call	VERB
app01-9397	56	12	neurons	neuron	NOUN
app01-9397	56	13	,	,	PUNCT
app01-9397	56	14	that	that	PRON
app01-9397	56	15	are	be	AUX
app01-9397	56	16	arranged	arrange	VERB
app01-9397	56	17	in	in	ADP
app01-9397	56	18	a	a	DET
app01-9397	56	19	layered	layered	ADJ
app01-9397	56	20	network	network	NOUN
app01-9397	56	21	.	.	PUNCT
app01-9397	57	1	in	in	ADP
app01-9397	57	2	this	this	DET
app01-9397	57	3	case	case	NOUN
app01-9397	57	4	,	,	PUNCT
app01-9397	57	5	a	a	DET
app01-9397	57	6	topology	topology	NOUN
app01-9397	57	7	is	be	AUX
app01-9397	57	8	used	use	VERB
app01-9397	57	9	where	where	SCONJ
app01-9397	57	10	each	each	DET
app01-9397	57	11	neuron	neuron	NOUN
app01-9397	57	12	in	in	ADP
app01-9397	57	13	one	one	NUM
app01-9397	57	14	layer	layer	NOUN
app01-9397	57	15	is	be	AUX
app01-9397	57	16	connected	connect	VERB
app01-9397	57	17	to	to	ADP
app01-9397	57	18	every	every	DET
app01-9397	57	19	neuron	neuron	NOUN
app01-9397	57	20	in	in	ADP
app01-9397	57	21	the	the	DET
app01-9397	57	22	neighboring	neighboring	NOUN
app01-9397	57	23	layers	layer	NOUN
app01-9397	57	24	,	,	PUNCT
app01-9397	57	25	forming	form	VERB
app01-9397	57	26	a	a	DET
app01-9397	57	27	so	so	ADV
app01-9397	57	28	called	call	VERB
app01-9397	57	29	(	(	PUNCT
app01-9397	57	30	fully	fully	ADV
app01-9397	57	31	connected	connect	VERB
app01-9397	57	32	)	)	PUNCT
app01-9397	57	33	feed	feed	NOUN
app01-9397	57	34	-	-	PUNCT
app01-9397	57	35	forward	forward	NOUN
app01-9397	57	36	neural	neural	ADJ
app01-9397	57	37	network	network	NOUN
app01-9397	57	38	.	.	PUNCT
app01-9397	58	1	each	each	DET
app01-9397	58	2	connection	connection	NOUN
app01-9397	58	3	between	between	ADP
app01-9397	58	4	two	two	NUM
app01-9397	58	5	neurons	neuron	NOUN
app01-9397	58	6	provides	provide	VERB
app01-9397	58	7	a	a	DET
app01-9397	58	8	degree	degree	NOUN
app01-9397	58	9	of	of	ADP
app01-9397	58	10	freedom	freedom	NOUN
app01-9397	58	11	for	for	ADP
app01-9397	58	12	the	the	DET
app01-9397	58	13	fitting	fitting	ADJ
app01-9397	58	14	process	process	NOUN
app01-9397	58	15	[	[	X
app01-9397	58	16	6	6	NUM
app01-9397	58	17	,	,	PUNCT
app01-9397	58	18	7	7	NUM
app01-9397	58	19	]	]	PUNCT
app01-9397	58	20	.	.	PUNCT
app01-9397	59	1	a	a	DET
app01-9397	59	2	schematic	schematic	ADJ
app01-9397	59	3	of	of	ADP
app01-9397	59	4	the	the	DET
app01-9397	59	5	topology	topology	NOUN
app01-9397	59	6	of	of	ADP
app01-9397	59	7	such	such	ADJ
app01-9397	59	8	networks	network	NOUN
app01-9397	59	9	is	be	AUX
app01-9397	59	10	shown	show	VERB
app01-9397	59	11	in	in	ADP
app01-9397	59	12	figure	figure	NOUN
app01-9397	59	13	3	3	NUM
app01-9397	59	14	.	.	PUNCT
app01-9397	60	1	support	support	NOUN
app01-9397	60	2	vector	vector	NOUN
app01-9397	60	3	regression	regression	NOUN
app01-9397	60	4	(	(	PUNCT
app01-9397	60	5	svr	svr	PROPN
app01-9397	60	6	)	)	PUNCT
app01-9397	60	7	can	can	AUX
app01-9397	60	8	be	be	AUX
app01-9397	60	9	viewed	view	VERB
app01-9397	60	10	–	–	PUNCT
app01-9397	60	11	in	in	ADP
app01-9397	60	12	a	a	DET
app01-9397	60	13	simplified	simplified	ADJ
app01-9397	60	14	manner	manner	NOUN
app01-9397	60	15	–	–	PUNCT
app01-9397	60	16	as	as	ADP
app01-9397	60	17	a	a	DET
app01-9397	60	18	generalized	generalized	ADJ
app01-9397	60	19	version	version	NOUN
app01-9397	60	20	of	of	ADP
app01-9397	60	21	linear	linear	ADJ
app01-9397	60	22	regression	regression	NOUN
app01-9397	60	23	that	that	PRON
app01-9397	60	24	is	be	AUX
app01-9397	60	25	not	not	PART
app01-9397	60	26	limited	limit	VERB
app01-9397	60	27	to	to	AUX
app01-9397	60	28	linear	linear	VERB
app01-9397	60	29	problems	problem	NOUN
app01-9397	60	30	[	[	X
app01-9397	60	31	8	8	NUM
app01-9397	60	32	]	]	PUNCT
app01-9397	60	33	.	.	PUNCT
app01-9397	61	1	a	a	DET
app01-9397	61	2	deeper	deep	ADJ
app01-9397	61	3	explanation	explanation	NOUN
app01-9397	61	4	of	of	ADP
app01-9397	61	5	svr	svr	PROPN
app01-9397	61	6	is	be	AUX
app01-9397	61	7	omitted	omit	VERB
app01-9397	61	8	at	at	ADP
app01-9397	61	9	this	this	DET
app01-9397	61	10	point	point	NOUN
app01-9397	61	11	,	,	PUNCT
app01-9397	61	12	since	since	SCONJ
app01-9397	61	13	this	this	DET
app01-9397	61	14	model	model	NOUN
app01-9397	61	15	proved	prove	VERB
app01-9397	61	16	to	to	PART
app01-9397	61	17	be	be	AUX
app01-9397	61	18	not	not	PART
app01-9397	61	19	suitable	suitable	ADJ
app01-9397	61	20	for	for	ADP
app01-9397	61	21	the	the	DET
app01-9397	61	22	task	task	NOUN
app01-9397	61	23	at	at	ADP
app01-9397	61	24	hand	hand	NOUN
app01-9397	61	25	.	.	PUNCT
app01-9397	62	1	k	k	X
app01-9397	62	2	-	-	PUNCT
app01-9397	62	3	nearest	near	ADJ
app01-9397	62	4	-	-	PUNCT
app01-9397	62	5	neighbor	neighbor	NOUN
app01-9397	62	6	regression	regression	NOUN
app01-9397	62	7	(	(	PUNCT
app01-9397	62	8	knnr	knnr	NOUN
app01-9397	62	9	)	)	PUNCT
app01-9397	62	10	and	and	CCONJ
app01-9397	62	11	xgboost	xgboost	ADV
app01-9397	62	12	(	(	PUNCT
app01-9397	62	13	xgb	xgb	NUM
app01-9397	62	14	)	)	PUNCT
app01-9397	62	15	represent	represent	VERB
app01-9397	62	16	the	the	DET
app01-9397	62	17	class	class	NOUN
app01-9397	62	18	of	of	ADP
app01-9397	62	19	non	non	ADJ
app01-9397	62	20	-	-	ADJ
app01-9397	62	21	parametric	parametric	ADJ
app01-9397	62	22	models	model	NOUN
app01-9397	62	23	.	.	PUNCT
app01-9397	63	1	these	these	DET
app01-9397	63	2	models	model	NOUN
app01-9397	63	3	do	do	AUX
app01-9397	63	4	not	not	PART
app01-9397	63	5	fit	fit	VERB
app01-9397	63	6	internal	internal	ADJ
app01-9397	63	7	parameters	parameter	NOUN
app01-9397	63	8	to	to	ADP
app01-9397	63	9	the	the	DET
app01-9397	63	10	data	datum	NOUN
app01-9397	63	11	.	.	PUNCT
app01-9397	64	1	rather	rather	ADV
app01-9397	64	2	,	,	PUNCT
app01-9397	64	3	predictions	prediction	NOUN
app01-9397	64	4	are	be	AUX
app01-9397	64	5	made	make	VERB
app01-9397	64	6	based	base	VERB
app01-9397	64	7	on	on	ADP
app01-9397	64	8	known	known	ADJ
app01-9397	64	9	data	datum	NOUN
app01-9397	64	10	directly	directly	ADV
app01-9397	64	11	.	.	PUNCT
app01-9397	65	1	therefore	therefore	ADV
app01-9397	65	2	,	,	PUNCT
app01-9397	65	3	changing	change	VERB
app01-9397	65	4	the	the	DET
app01-9397	65	5	training	training	NOUN
app01-9397	65	6	data	datum	NOUN
app01-9397	65	7	automatically	automatically	ADV
app01-9397	65	8	changes	change	VERB
app01-9397	65	9	the	the	DET
app01-9397	65	10	model	model	NOUN
app01-9397	65	11	’s	’s	PART
app01-9397	65	12	predictions	prediction	NOUN
app01-9397	65	13	.	.	PUNCT
app01-9397	66	1	knnr	knnr	NOUN
app01-9397	66	2	predicts	predict	NOUN
app01-9397	66	3	stresses	stress	NOUN
app01-9397	66	4	for	for	ADP
app01-9397	66	5	a	a	DET
app01-9397	66	6	new	new	ADJ
app01-9397	66	7	combination	combination	NOUN
app01-9397	66	8	of	of	ADP
app01-9397	66	9	input	input	NOUN
app01-9397	66	10	parameters	parameter	NOUN
app01-9397	66	11	,	,	PUNCT
app01-9397	66	12	also	also	ADV
app01-9397	66	13	called	call	VERB
app01-9397	66	14	features	feature	NOUN
app01-9397	66	15	,	,	PUNCT
app01-9397	66	16	by	by	ADP
app01-9397	66	17	identifying	identify	VERB
app01-9397	66	18	the	the	DET
app01-9397	66	19	k	k	PROPN
app01-9397	66	20	closest	close	ADV
app01-9397	66	21	known	know	VERB
app01-9397	66	22	examples	example	NOUN
app01-9397	66	23	from	from	ADP
app01-9397	66	24	the	the	DET
app01-9397	66	25	input	input	NOUN
app01-9397	66	26	space	space	NOUN
app01-9397	66	27	of	of	ADP
app01-9397	66	28	the	the	DET
app01-9397	66	29	training	training	NOUN
app01-9397	66	30	data	datum	NOUN
app01-9397	66	31	.	.	PUNCT
app01-9397	67	1	the	the	DET
app01-9397	67	2	predicted	predict	VERB
app01-9397	67	3	stress	stress	NOUN
app01-9397	67	4	value	value	NOUN
app01-9397	67	5	,	,	PUNCT
app01-9397	67	6	then	then	ADV
app01-9397	67	7	,	,	PUNCT
app01-9397	67	8	is	be	AUX
app01-9397	67	9	the	the	DET
app01-9397	67	10	average	average	NOUN
app01-9397	67	11	of	of	ADP
app01-9397	67	12	these	these	DET
app01-9397	67	13	k	k	PROPN
app01-9397	67	14	known	know	VERB
app01-9397	67	15	stress	stress	NOUN
app01-9397	67	16	values	value	NOUN
app01-9397	67	17	[	[	X
app01-9397	67	18	9	9	NUM
app01-9397	67	19	]	]	PUNCT
app01-9397	67	20	.	.	PUNCT
app01-9397	68	1	33	33	NUM
app01-9397	68	2	s.	s.	PROPN
app01-9397	68	3	grednev	grednev	PROPN
app01-9397	68	4	,	,	PUNCT
app01-9397	68	5	h.	h.	PROPN
app01-9397	68	6	s.	s.	PROPN
app01-9397	68	7	steude	steude	PROPN
app01-9397	68	8	,	,	PUNCT
app01-9397	68	9	s.	s.	PROPN
app01-9397	68	10	bronder	bronder	PROPN
app01-9397	68	11	et	et	PROPN
app01-9397	68	12	al	al	PROPN
app01-9397	68	13	.	.	PROPN
app01-9397	68	14	acta	acta	PROPN
app01-9397	68	15	polytechnica	polytechnica	PROPN
app01-9397	68	16	ctu	ctu	PROPN
app01-9397	68	17	proceedings	proceeding	NOUN
app01-9397	68	18	figure	figure	VERB
app01-9397	68	19	4	4	NUM
app01-9397	68	20	.	.	PUNCT
app01-9397	69	1	distribution	distribution	NOUN
app01-9397	69	2	of	of	ADP
app01-9397	69	3	mean	mean	ADJ
app01-9397	69	4	mass	mass	PROPN
app01-9397	69	5	normalized	normalize	VERB
app01-9397	69	6	stress	stress	ADJ
app01-9397	69	7	values	value	NOUN
app01-9397	69	8	for	for	ADP
app01-9397	69	9	all	all	DET
app01-9397	69	10	130	130	NUM
app01-9397	69	11	available	available	ADJ
app01-9397	69	12	curves	curve	NOUN
app01-9397	69	13	.	.	PUNCT
app01-9397	70	1	xgboost	xgboost	PROPN
app01-9397	70	2	is	be	AUX
app01-9397	70	3	an	an	DET
app01-9397	70	4	advanced	advanced	ADJ
app01-9397	70	5	and	and	CCONJ
app01-9397	70	6	optimized	optimize	VERB
app01-9397	70	7	implementation	implementation	NOUN
app01-9397	70	8	of	of	ADP
app01-9397	70	9	decision	decision	NOUN
app01-9397	70	10	trees	tree	NOUN
app01-9397	70	11	[	[	X
app01-9397	70	12	10	10	NUM
app01-9397	70	13	]	]	PUNCT
app01-9397	70	14	.	.	PUNCT
app01-9397	71	1	decision	decision	NOUN
app01-9397	71	2	trees	tree	NOUN
app01-9397	71	3	consist	consist	VERB
app01-9397	71	4	of	of	ADP
app01-9397	71	5	a	a	DET
app01-9397	71	6	set	set	NOUN
app01-9397	71	7	of	of	ADP
app01-9397	71	8	“	"	PUNCT
app01-9397	71	9	if	if	SCONJ
app01-9397	71	10	–	–	PUNCT
app01-9397	71	11	then	then	ADV
app01-9397	71	12	”	"	PUNCT
app01-9397	71	13	statements	statement	NOUN
app01-9397	71	14	,	,	PUNCT
app01-9397	71	15	also	also	ADV
app01-9397	71	16	called	call	VERB
app01-9397	71	17	decision	decision	NOUN
app01-9397	71	18	rules	rule	NOUN
app01-9397	71	19	,	,	PUNCT
app01-9397	71	20	that	that	PRON
app01-9397	71	21	divide	divide	VERB
app01-9397	71	22	the	the	DET
app01-9397	71	23	input	input	NOUN
app01-9397	71	24	space	space	NOUN
app01-9397	71	25	into	into	ADP
app01-9397	71	26	subdomains	subdomain	NOUN
app01-9397	71	27	or	or	CCONJ
app01-9397	71	28	partitions	partition	NOUN
app01-9397	71	29	.	.	PUNCT
app01-9397	72	1	predictions	prediction	NOUN
app01-9397	72	2	are	be	AUX
app01-9397	72	3	then	then	ADV
app01-9397	72	4	made	make	VERB
app01-9397	72	5	based	base	VERB
app01-9397	72	6	on	on	ADP
app01-9397	72	7	the	the	DET
app01-9397	72	8	partition	partition	NOUN
app01-9397	72	9	to	to	PART
app01-9397	72	10	which	which	PRON
app01-9397	72	11	the	the	DET
app01-9397	72	12	sample	sample	NOUN
app01-9397	72	13	at	at	ADP
app01-9397	72	14	hand	hand	NOUN
app01-9397	72	15	belongs	belong	VERB
app01-9397	72	16	,	,	PUNCT
app01-9397	72	17	typically	typically	ADV
app01-9397	72	18	predicting	predict	VERB
app01-9397	72	19	the	the	DET
app01-9397	72	20	average	average	ADJ
app01-9397	72	21	target	target	NOUN
app01-9397	72	22	value	value	NOUN
app01-9397	72	23	of	of	ADP
app01-9397	72	24	the	the	DET
app01-9397	72	25	partition	partition	NOUN
app01-9397	72	26	[	[	X
app01-9397	72	27	8	8	NUM
app01-9397	72	28	]	]	PUNCT
app01-9397	72	29	.	.	PUNCT
app01-9397	73	1	for	for	ADP
app01-9397	73	2	the	the	DET
app01-9397	73	3	additional	additional	ADJ
app01-9397	73	4	sc	sc	NOUN
app01-9397	73	5	approach	approach	NOUN
app01-9397	73	6	mentioned	mention	VERB
app01-9397	73	7	at	at	ADP
app01-9397	73	8	the	the	DET
app01-9397	73	9	end	end	NOUN
app01-9397	73	10	of	of	ADP
app01-9397	73	11	section	section	NOUN
app01-9397	73	12	2.3	2.3	NUM
app01-9397	73	13	,	,	PUNCT
app01-9397	73	14	temporal	temporal	ADJ
app01-9397	73	15	convolutional	convolutional	ADJ
app01-9397	73	16	networks	network	NOUN
app01-9397	73	17	(	(	PUNCT
app01-9397	73	18	tcns	tcns	NOUN
app01-9397	73	19	)	)	PUNCT
app01-9397	74	1	[	[	X
app01-9397	74	2	11	11	NUM
app01-9397	74	3	]	]	PUNCT
app01-9397	74	4	are	be	AUX
app01-9397	74	5	employed	employ	VERB
app01-9397	74	6	to	to	PART
app01-9397	74	7	generate	generate	VERB
app01-9397	74	8	the	the	DET
app01-9397	74	9	sequence	sequence	NOUN
app01-9397	74	10	from	from	ADP
app01-9397	74	11	the	the	DET
app01-9397	74	12	input	input	NOUN
app01-9397	74	13	features	feature	NOUN
app01-9397	74	14	.	.	PUNCT
app01-9397	75	1	tcns	tcns	PROPN
app01-9397	75	2	are	be	AUX
app01-9397	75	3	a	a	DET
app01-9397	75	4	class	class	NOUN
app01-9397	75	5	of	of	ADP
app01-9397	75	6	neural	neural	ADJ
app01-9397	75	7	networks	network	NOUN
app01-9397	75	8	that	that	PRON
app01-9397	75	9	are	be	AUX
app01-9397	75	10	particularly	particularly	ADV
app01-9397	75	11	well	well	ADV
app01-9397	75	12	-	-	PUNCT
app01-9397	75	13	suited	suit	VERB
app01-9397	75	14	for	for	ADP
app01-9397	75	15	sequence	sequence	NOUN
app01-9397	75	16	modeling	modeling	NOUN
app01-9397	75	17	tasks	task	NOUN
app01-9397	75	18	,	,	PUNCT
app01-9397	75	19	as	as	SCONJ
app01-9397	75	20	they	they	PRON
app01-9397	75	21	make	make	VERB
app01-9397	75	22	use	use	NOUN
app01-9397	75	23	of	of	ADP
app01-9397	75	24	temporal	temporal	ADJ
app01-9397	75	25	convolutions	convolution	NOUN
app01-9397	75	26	to	to	PART
app01-9397	75	27	capture	capture	VERB
app01-9397	75	28	dependencies	dependency	NOUN
app01-9397	75	29	of	of	ADP
app01-9397	75	30	different	different	ADJ
app01-9397	75	31	lengths	length	NOUN
app01-9397	75	32	in	in	ADP
app01-9397	75	33	the	the	DET
app01-9397	75	34	data	data	NOUN
app01-9397	75	35	.	.	PUNCT
app01-9397	76	1	through	through	ADP
app01-9397	76	2	multiple	multiple	ADJ
app01-9397	76	3	layers	layer	NOUN
app01-9397	76	4	of	of	ADP
app01-9397	76	5	dilated	dilated	ADJ
app01-9397	76	6	convolutions	convolution	NOUN
app01-9397	76	7	,	,	PUNCT
app01-9397	76	8	tcns	tcns	PROPN
app01-9397	76	9	can	can	AUX
app01-9397	76	10	effectively	effectively	ADV
app01-9397	76	11	learn	learn	VERB
app01-9397	76	12	and	and	CCONJ
app01-9397	76	13	represent	represent	VERB
app01-9397	76	14	complex	complex	ADJ
app01-9397	76	15	patterns	pattern	NOUN
app01-9397	76	16	across	across	ADP
app01-9397	76	17	various	various	ADJ
app01-9397	76	18	time	time	NOUN
app01-9397	76	19	scales	scale	NOUN
app01-9397	76	20	in	in	ADP
app01-9397	76	21	the	the	DET
app01-9397	76	22	sequence	sequence	NOUN
app01-9397	76	23	,	,	PUNCT
app01-9397	76	24	making	make	VERB
app01-9397	76	25	them	they	PRON
app01-9397	76	26	a	a	DET
app01-9397	76	27	promising	promising	ADJ
app01-9397	76	28	choice	choice	NOUN
app01-9397	76	29	for	for	ADP
app01-9397	76	30	predicting	predict	VERB
app01-9397	76	31	the	the	DET
app01-9397	76	32	entire	entire	ADJ
app01-9397	76	33	stress	stress	NOUN
app01-9397	76	34	-	-	PUNCT
app01-9397	76	35	strain	strain	NOUN
app01-9397	76	36	curve	curve	NOUN
app01-9397	76	37	with	with	ADP
app01-9397	76	38	high	high	ADJ
app01-9397	76	39	resolution	resolution	NOUN
app01-9397	76	40	.	.	PUNCT
app01-9397	77	1	2.5	2.5	NUM
app01-9397	77	2	.	.	PUNCT
app01-9397	78	1	course	course	NOUN
app01-9397	78	2	of	of	ADP
app01-9397	78	3	action	action	NOUN
app01-9397	78	4	to	to	PART
app01-9397	78	5	provide	provide	VERB
app01-9397	78	6	a	a	DET
app01-9397	78	7	starting	starting	NOUN
app01-9397	78	8	point	point	NOUN
app01-9397	78	9	,	,	PUNCT
app01-9397	78	10	first	first	ADV
app01-9397	78	11	,	,	PUNCT
app01-9397	78	12	the	the	DET
app01-9397	78	13	four	four	NUM
app01-9397	78	14	main	main	ADJ
app01-9397	78	15	models	model	NOUN
app01-9397	78	16	introduced	introduce	VERB
app01-9397	78	17	in	in	ADP
app01-9397	78	18	section	section	NOUN
app01-9397	78	19	2.4	2.4	NUM
app01-9397	78	20	are	be	AUX
app01-9397	78	21	trained	train	VERB
app01-9397	78	22	on	on	ADP
app01-9397	78	23	the	the	DET
app01-9397	78	24	original	original	ADJ
app01-9397	78	25	5	5	NUM
app01-9397	78	26	features	feature	NOUN
app01-9397	78	27	,	,	PUNCT
app01-9397	78	28	namely	namely	ADV
app01-9397	78	29	size	size	NOUN
app01-9397	78	30	,	,	PUNCT
app01-9397	78	31	waist	waist	NOUN
app01-9397	78	32	,	,	PUNCT
app01-9397	78	33	length	length	NOUN
app01-9397	78	34	,	,	PUNCT
app01-9397	78	35	angle	angle	NOUN
app01-9397	78	36	and	and	CCONJ
app01-9397	78	37	strut	strut	NOUN
app01-9397	78	38	thickness	thickness	NOUN
app01-9397	78	39	,	,	PUNCT
app01-9397	78	40	using	use	VERB
app01-9397	78	41	the	the	DET
app01-9397	78	42	sp	sp	NOUN
app01-9397	78	43	and	and	CCONJ
app01-9397	78	44	the	the	DET
app01-9397	78	45	wc	wc	PROPN
app01-9397	78	46	approach	approach	NOUN
app01-9397	78	47	.	.	PUNCT
app01-9397	79	1	here	here	ADV
app01-9397	79	2	,	,	PUNCT
app01-9397	79	3	a	a	DET
app01-9397	79	4	rudimentary	rudimentary	ADJ
app01-9397	79	5	hyperparameter	hyperparameter	NOUN
app01-9397	79	6	optimization	optimization	NOUN
app01-9397	79	7	is	be	AUX
app01-9397	79	8	carried	carry	VERB
app01-9397	79	9	out	out	ADP
app01-9397	79	10	to	to	PART
app01-9397	79	11	obtain	obtain	VERB
app01-9397	79	12	parameters	parameter	NOUN
app01-9397	79	13	that	that	PRON
app01-9397	79	14	are	be	AUX
app01-9397	79	15	close	close	ADJ
app01-9397	79	16	to	to	ADP
app01-9397	79	17	the	the	DET
app01-9397	79	18	optimal	optimal	ADJ
app01-9397	79	19	choice	choice	NOUN
app01-9397	79	20	.	.	PUNCT
app01-9397	80	1	hyperparameters	hyperparameter	NOUN
app01-9397	80	2	are	be	AUX
app01-9397	80	3	parameters	parameter	NOUN
app01-9397	80	4	describing	describe	VERB
app01-9397	80	5	the	the	DET
app01-9397	80	6	architecture	architecture	NOUN
app01-9397	80	7	and/or	and/or	CCONJ
app01-9397	80	8	functionality	functionality	NOUN
app01-9397	80	9	of	of	ADP
app01-9397	80	10	the	the	DET
app01-9397	80	11	model	model	NOUN
app01-9397	80	12	.	.	PUNCT
app01-9397	81	1	using	use	VERB
app01-9397	81	2	these	these	DET
app01-9397	81	3	trained	train	VERB
app01-9397	81	4	models	model	NOUN
app01-9397	81	5	,	,	PUNCT
app01-9397	81	6	feature	feature	NOUN
app01-9397	81	7	engineering	engineering	NOUN
app01-9397	81	8	is	be	AUX
app01-9397	81	9	performed	perform	VERB
app01-9397	81	10	,	,	PUNCT
app01-9397	81	11	which	which	PRON
app01-9397	81	12	is	be	AUX
app01-9397	81	13	a	a	DET
app01-9397	81	14	set	set	NOUN
app01-9397	81	15	of	of	ADP
app01-9397	81	16	techniques	technique	NOUN
app01-9397	81	17	aiming	aim	VERB
app01-9397	81	18	at	at	ADP
app01-9397	81	19	creating	create	VERB
app01-9397	81	20	data	datum	NOUN
app01-9397	81	21	representations	representation	NOUN
app01-9397	81	22	that	that	PRON
app01-9397	81	23	make	make	VERB
app01-9397	81	24	the	the	DET
app01-9397	81	25	learning	learning	NOUN
app01-9397	81	26	process	process	NOUN
app01-9397	81	27	easier	easy	ADJ
app01-9397	81	28	.	.	PUNCT
app01-9397	82	1	for	for	ADP
app01-9397	82	2	this	this	DET
app01-9397	82	3	sake	sake	NOUN
app01-9397	82	4	,	,	PUNCT
app01-9397	82	5	a	a	DET
app01-9397	82	6	selection	selection	NOUN
app01-9397	82	7	of	of	ADP
app01-9397	82	8	feature	feature	NOUN
app01-9397	82	9	combinations	combination	NOUN
app01-9397	82	10	is	be	AUX
app01-9397	82	11	evaluated	evaluate	VERB
app01-9397	82	12	in	in	ADP
app01-9397	82	13	combination	combination	NOUN
app01-9397	82	14	with	with	ADP
app01-9397	82	15	every	every	DET
app01-9397	82	16	model	model	NOUN
app01-9397	82	17	regarding	regard	VERB
app01-9397	82	18	their	their	PRON
app01-9397	82	19	predictive	predictive	ADJ
app01-9397	82	20	performance	performance	NOUN
app01-9397	82	21	.	.	PUNCT
app01-9397	83	1	the	the	DET
app01-9397	83	2	feature	feature	NOUN
app01-9397	83	3	combinations	combination	NOUN
app01-9397	83	4	found	find	VERB
app01-9397	83	5	to	to	PART
app01-9397	83	6	lead	lead	VERB
app01-9397	83	7	to	to	ADP
app01-9397	83	8	best	good	ADJ
app01-9397	83	9	results	result	NOUN
app01-9397	83	10	–	–	PUNCT
app01-9397	83	11	which	which	PRON
app01-9397	83	12	can	can	AUX
app01-9397	83	13	be	be	AUX
app01-9397	83	14	different	different	ADJ
app01-9397	83	15	for	for	ADP
app01-9397	83	16	different	different	ADJ
app01-9397	83	17	models	model	NOUN
app01-9397	83	18	–	–	PUNCT
app01-9397	83	19	are	be	AUX
app01-9397	83	20	then	then	ADV
app01-9397	83	21	used	use	VERB
app01-9397	83	22	to	to	PART
app01-9397	83	23	retrain	retrain	VERB
app01-9397	83	24	the	the	DET
app01-9397	83	25	models	model	NOUN
app01-9397	83	26	and	and	CCONJ
app01-9397	83	27	perform	perform	VERB
app01-9397	83	28	a	a	DET
app01-9397	83	29	more	more	ADV
app01-9397	83	30	extensive	extensive	ADJ
app01-9397	83	31	hyperparameter	hyperparameter	NOUN
app01-9397	83	32	optimization	optimization	NOUN
app01-9397	83	33	,	,	PUNCT
app01-9397	83	34	resulting	result	VERB
app01-9397	83	35	in	in	ADP
app01-9397	83	36	figure	figure	NOUN
app01-9397	83	37	5	5	NUM
app01-9397	83	38	.	.	PUNCT
app01-9397	83	39	performance	performance	NOUN
app01-9397	83	40	of	of	ADP
app01-9397	83	41	each	each	DET
app01-9397	83	42	model	model	NOUN
app01-9397	83	43	-	-	PUNCT
app01-9397	83	44	approach	approach	NOUN
app01-9397	83	45	combination	combination	NOUN
app01-9397	83	46	on	on	ADP
app01-9397	83	47	the	the	DET
app01-9397	83	48	final	final	ADJ
app01-9397	83	49	test	test	NOUN
app01-9397	83	50	set	set	NOUN
app01-9397	83	51	.	.	PUNCT
app01-9397	84	1	error	error	NOUN
app01-9397	84	2	bars	bar	NOUN
app01-9397	84	3	denote	denote	VERB
app01-9397	84	4	the	the	DET
app01-9397	84	5	standard	standard	ADJ
app01-9397	84	6	deviation	deviation	NOUN
app01-9397	84	7	with	with	ADP
app01-9397	84	8	regard	regard	NOUN
app01-9397	84	9	to	to	ADP
app01-9397	84	10	individual	individual	ADJ
app01-9397	84	11	curves	curve	NOUN
app01-9397	84	12	.	.	PUNCT
app01-9397	85	1	the	the	DET
app01-9397	85	2	final	final	ADJ
app01-9397	85	3	optimized	optimize	VERB
app01-9397	85	4	models	model	NOUN
app01-9397	85	5	.	.	PUNCT
app01-9397	86	1	in	in	ADP
app01-9397	86	2	order	order	NOUN
app01-9397	86	3	to	to	PART
app01-9397	86	4	estimate	estimate	VERB
app01-9397	86	5	model	model	NOUN
app01-9397	86	6	performance	performance	NOUN
app01-9397	86	7	reliably	reliably	ADV
app01-9397	86	8	while	while	SCONJ
app01-9397	86	9	trying	try	VERB
app01-9397	86	10	to	to	PART
app01-9397	86	11	prevent	prevent	VERB
app01-9397	86	12	overfitting	overfitte	VERB
app01-9397	86	13	,	,	PUNCT
app01-9397	86	14	i.e.	i.e.	X
app01-9397	86	15	good	good	ADJ
app01-9397	86	16	performance	performance	NOUN
app01-9397	86	17	on	on	ADP
app01-9397	86	18	known	know	VERB
app01-9397	86	19	data	datum	NOUN
app01-9397	86	20	and	and	CCONJ
app01-9397	86	21	bad	bad	ADJ
app01-9397	86	22	performance	performance	NOUN
app01-9397	86	23	on	on	ADP
app01-9397	86	24	new	new	ADJ
app01-9397	86	25	data	datum	NOUN
app01-9397	86	26	,	,	PUNCT
app01-9397	86	27	throughout	throughout	ADP
app01-9397	86	28	the	the	DET
app01-9397	86	29	model	model	NOUN
app01-9397	86	30	optimization	optimization	NOUN
app01-9397	86	31	process	process	NOUN
app01-9397	86	32	a	a	DET
app01-9397	86	33	cross	cross	NOUN
app01-9397	86	34	validation	validation	NOUN
app01-9397	86	35	(	(	PUNCT
app01-9397	86	36	cv	cv	NOUN
app01-9397	86	37	)	)	PUNCT
app01-9397	86	38	procedure	procedure	NOUN
app01-9397	86	39	is	be	AUX
app01-9397	86	40	implemented	implement	VERB
app01-9397	86	41	.	.	PUNCT
app01-9397	87	1	the	the	DET
app01-9397	87	2	data	datum	NOUN
app01-9397	87	3	set	set	VERB
app01-9397	87	4	at	at	ADP
app01-9397	87	5	hand	hand	NOUN
app01-9397	87	6	is	be	AUX
app01-9397	87	7	imbalanced	imbalance	VERB
app01-9397	87	8	with	with	ADP
app01-9397	87	9	high	high	ADJ
app01-9397	87	10	-	-	PUNCT
app01-9397	87	11	stress	stress	NOUN
app01-9397	87	12	curves	curve	NOUN
app01-9397	87	13	being	be	AUX
app01-9397	87	14	highly	highly	ADV
app01-9397	87	15	underrepresented	underrepresented	ADJ
app01-9397	87	16	,	,	PUNCT
app01-9397	87	17	see	see	VERB
app01-9397	87	18	figure	figure	NOUN
app01-9397	87	19	4	4	NUM
app01-9397	87	20	.	.	PUNCT
app01-9397	88	1	therefore	therefore	ADV
app01-9397	88	2	,	,	PUNCT
app01-9397	88	3	the	the	DET
app01-9397	88	4	cv	cv	PROPN
app01-9397	88	5	procedure	procedure	NOUN
app01-9397	88	6	is	be	AUX
app01-9397	88	7	stratified	stratify	VERB
app01-9397	88	8	to	to	PART
app01-9397	88	9	guarantee	guarantee	VERB
app01-9397	88	10	that	that	SCONJ
app01-9397	88	11	the	the	DET
app01-9397	88	12	distribution	distribution	NOUN
app01-9397	88	13	of	of	ADP
app01-9397	88	14	mean	mean	ADJ
app01-9397	88	15	stress	stress	NOUN
app01-9397	88	16	values	value	NOUN
app01-9397	88	17	while	while	SCONJ
app01-9397	88	18	splitting	split	VERB
app01-9397	88	19	data	datum	NOUN
app01-9397	88	20	(	(	PUNCT
app01-9397	88	21	train	train	NOUN
app01-9397	88	22	,	,	PUNCT
app01-9397	88	23	validation	validation	NOUN
app01-9397	88	24	and	and	CCONJ
app01-9397	88	25	test	test	NOUN
app01-9397	88	26	)	)	PUNCT
app01-9397	88	27	stays	stay	VERB
app01-9397	88	28	as	as	ADV
app01-9397	88	29	close	close	ADJ
app01-9397	88	30	as	as	ADP
app01-9397	88	31	possible	possible	ADJ
app01-9397	88	32	to	to	ADP
app01-9397	88	33	the	the	DET
app01-9397	88	34	distribution	distribution	NOUN
app01-9397	88	35	in	in	ADP
app01-9397	88	36	the	the	DET
app01-9397	88	37	original	original	ADJ
app01-9397	88	38	data	datum	NOUN
app01-9397	88	39	set	set	VERB
app01-9397	88	40	.	.	PUNCT
app01-9397	89	1	for	for	ADP
app01-9397	89	2	every	every	DET
app01-9397	89	3	model	model	NOUN
app01-9397	89	4	,	,	PUNCT
app01-9397	89	5	a	a	DET
app01-9397	89	6	4	4	NUM
app01-9397	89	7	-	-	ADJ
app01-9397	89	8	fold	fold	ADJ
app01-9397	89	9	-	-	PUNCT
app01-9397	89	10	cv	cv	NOUN
app01-9397	89	11	is	be	AUX
app01-9397	89	12	implemented	implement	VERB
app01-9397	89	13	.	.	PUNCT
app01-9397	90	1	the	the	DET
app01-9397	90	2	splitting	splitting	NOUN
app01-9397	90	3	procedure	procedure	NOUN
app01-9397	90	4	being	be	AUX
app01-9397	90	5	repeated	repeat	VERB
app01-9397	90	6	three	three	NUM
app01-9397	90	7	times	time	NOUN
app01-9397	90	8	,	,	PUNCT
app01-9397	90	9	resulting	result	VERB
app01-9397	90	10	in	in	ADP
app01-9397	90	11	12	12	NUM
app01-9397	90	12	different	different	ADJ
app01-9397	90	13	train	train	NOUN
app01-9397	90	14	/	/	SYM
app01-9397	90	15	validation	validation	NOUN
app01-9397	90	16	splits	split	VERB
app01-9397	90	17	with	with	ADP
app01-9397	90	18	a	a	DET
app01-9397	90	19	size	size	NOUN
app01-9397	90	20	ratio	ratio	NOUN
app01-9397	90	21	of	of	ADP
app01-9397	90	22	75	75	NUM
app01-9397	90	23	to	to	PART
app01-9397	90	24	25	25	NUM
app01-9397	90	25	.	.	PUNCT
app01-9397	91	1	at	at	ADP
app01-9397	91	2	the	the	DET
app01-9397	91	3	very	very	ADJ
app01-9397	91	4	end	end	NOUN
app01-9397	91	5	of	of	ADP
app01-9397	91	6	the	the	DET
app01-9397	91	7	project	project	NOUN
app01-9397	91	8	,	,	PUNCT
app01-9397	91	9	all	all	DET
app01-9397	91	10	optimized	optimize	VERB
app01-9397	91	11	models	model	NOUN
app01-9397	91	12	are	be	AUX
app01-9397	91	13	trained	train	VERB
app01-9397	91	14	on	on	ADP
app01-9397	91	15	all	all	DET
app01-9397	91	16	115	115	NUM
app01-9397	91	17	curves	curve	NOUN
app01-9397	91	18	available	available	ADJ
app01-9397	91	19	for	for	ADP
app01-9397	91	20	the	the	DET
app01-9397	91	21	modeling	modeling	NOUN
app01-9397	91	22	process	process	NOUN
app01-9397	91	23	and	and	CCONJ
app01-9397	91	24	then	then	ADV
app01-9397	91	25	used	use	VERB
app01-9397	91	26	to	to	PART
app01-9397	91	27	make	make	VERB
app01-9397	91	28	predictions	prediction	NOUN
app01-9397	91	29	for	for	ADP
app01-9397	91	30	15	15	NUM
app01-9397	91	31	neverseen	neverseen	NUM
app01-9397	91	32	-	-	PUNCT
app01-9397	91	33	before	before	NOUN
app01-9397	91	34	curves	curve	NOUN
app01-9397	91	35	–	–	PUNCT
app01-9397	91	36	and	and	CCONJ
app01-9397	91	37	therefore	therefore	ADV
app01-9397	91	38	until	until	ADP
app01-9397	91	39	now	now	ADV
app01-9397	91	40	unknown	unknown	ADJ
app01-9397	91	41	geometry	geometry	NOUN
app01-9397	91	42	describing	describe	VERB
app01-9397	91	43	input	input	NOUN
app01-9397	91	44	parameters	parameter	NOUN
app01-9397	91	45	–	–	PUNCT
app01-9397	91	46	in	in	ADP
app01-9397	91	47	an	an	DET
app01-9397	91	48	attempt	attempt	NOUN
app01-9397	91	49	to	to	PART
app01-9397	91	50	simulate	simulate	VERB
app01-9397	91	51	the	the	DET
app01-9397	91	52	real	real	ADJ
app01-9397	91	53	application	application	NOUN
app01-9397	91	54	scenario	scenario	NOUN
app01-9397	91	55	.	.	PUNCT
app01-9397	92	1	3	3	X
app01-9397	92	2	.	.	NOUN
app01-9397	92	3	results	result	NOUN
app01-9397	92	4	and	and	CCONJ
app01-9397	92	5	discussion	discussion	NOUN
app01-9397	92	6	the	the	DET
app01-9397	92	7	summarized	summarize	VERB
app01-9397	92	8	performances	performance	NOUN
app01-9397	92	9	of	of	ADP
app01-9397	92	10	all	all	DET
app01-9397	92	11	trained	train	VERB
app01-9397	92	12	models	model	NOUN
app01-9397	92	13	can	can	AUX
app01-9397	92	14	be	be	AUX
app01-9397	92	15	seen	see	VERB
app01-9397	92	16	in	in	ADP
app01-9397	92	17	figure	figure	NOUN
app01-9397	92	18	5	5	NUM
app01-9397	92	19	.	.	PUNCT
app01-9397	93	1	it	it	PRON
app01-9397	93	2	is	be	AUX
app01-9397	93	3	apparent	apparent	ADJ
app01-9397	93	4	that	that	SCONJ
app01-9397	93	5	the	the	DET
app01-9397	93	6	choice	choice	NOUN
app01-9397	93	7	of	of	ADP
app01-9397	93	8	the	the	DET
app01-9397	93	9	approach	approach	NOUN
app01-9397	93	10	,	,	PUNCT
app01-9397	93	11	meaning	meaning	NOUN
app01-9397	93	12	sp	sp	ADP
app01-9397	93	13	versus	versus	X
app01-9397	93	14	wc	wc	PROPN
app01-9397	93	15	,	,	PUNCT
app01-9397	93	16	had	have	VERB
app01-9397	93	17	no	no	DET
app01-9397	93	18	significant	significant	ADJ
app01-9397	93	19	effect	effect	NOUN
app01-9397	93	20	on	on	ADP
app01-9397	93	21	the	the	DET
app01-9397	93	22	predictive	predictive	ADJ
app01-9397	93	23	performance	performance	NOUN
app01-9397	93	24	of	of	ADP
app01-9397	93	25	the	the	DET
app01-9397	93	26	models	model	NOUN
app01-9397	93	27	.	.	PUNCT
app01-9397	94	1	the	the	DET
app01-9397	94	2	particularly	particularly	ADV
app01-9397	94	3	bad	bad	ADJ
app01-9397	94	4	performance	performance	NOUN
app01-9397	94	5	of	of	ADP
app01-9397	94	6	knnr	knnr	NOUN
app01-9397	94	7	(	(	PUNCT
app01-9397	94	8	sp	sp	NOUN
app01-9397	94	9	)	)	PUNCT
app01-9397	94	10	can	can	AUX
app01-9397	94	11	only	only	ADV
app01-9397	94	12	be	be	AUX
app01-9397	94	13	explained	explain	VERB
app01-9397	94	14	with	with	ADP
app01-9397	94	15	overfitting	overfitte	VERB
app01-9397	94	16	,	,	PUNCT
app01-9397	94	17	even	even	ADV
app01-9397	94	18	though	though	SCONJ
app01-9397	94	19	all	all	DET
app01-9397	94	20	possible	possible	ADJ
app01-9397	94	21	precautions	precaution	NOUN
app01-9397	94	22	were	be	AUX
app01-9397	94	23	taken	take	VERB
app01-9397	94	24	.	.	PUNCT
app01-9397	95	1	here	here	ADV
app01-9397	95	2	,	,	PUNCT
app01-9397	95	3	one	one	NUM
app01-9397	95	4	particular	particular	ADJ
app01-9397	95	5	curve	curve	NOUN
app01-9397	95	6	of	of	ADP
app01-9397	95	7	the	the	DET
app01-9397	95	8	final	final	ADJ
app01-9397	95	9	test	test	NOUN
app01-9397	95	10	set	set	NOUN
app01-9397	95	11	,	,	PUNCT
app01-9397	95	12	was	be	AUX
app01-9397	95	13	predicted	predict	VERB
app01-9397	95	14	extremely	extremely	ADV
app01-9397	95	15	bad	bad	ADJ
app01-9397	95	16	,	,	PUNCT
app01-9397	95	17	drastically	drastically	ADV
app01-9397	95	18	worsening	worsen	VERB
app01-9397	95	19	the	the	DET
app01-9397	95	20	average	average	ADJ
app01-9397	95	21	performance	performance	NOUN
app01-9397	95	22	of	of	ADP
app01-9397	95	23	the	the	DET
app01-9397	95	24	model	model	NOUN
app01-9397	95	25	.	.	PUNCT
app01-9397	96	1	in	in	ADP
app01-9397	96	2	all	all	DET
app01-9397	96	3	prior	prior	ADJ
app01-9397	96	4	stages	stage	NOUN
app01-9397	96	5	of	of	ADP
app01-9397	96	6	the	the	DET
app01-9397	96	7	model	model	NOUN
app01-9397	96	8	building	building	NOUN
app01-9397	96	9	process	process	NOUN
app01-9397	96	10	its	its	PRON
app01-9397	96	11	performance	performance	NOUN
app01-9397	96	12	was	be	AUX
app01-9397	96	13	comparable	comparable	ADJ
app01-9397	96	14	to	to	ADP
app01-9397	96	15	the	the	DET
app01-9397	96	16	other	other	ADJ
app01-9397	96	17	models	model	NOUN
app01-9397	96	18	,	,	PUNCT
app01-9397	96	19	especially	especially	ADV
app01-9397	96	20	to	to	ADP
app01-9397	96	21	knnr	knnr	NOUN
app01-9397	96	22	(	(	PUNCT
app01-9397	96	23	wc	wc	PROPN
app01-9397	96	24	)	)	PUNCT
app01-9397	96	25	.	.	PUNCT
app01-9397	97	1	svr	svr	PROPN
app01-9397	97	2	(	(	PUNCT
app01-9397	97	3	sp	sp	NOUN
app01-9397	97	4	)	)	PUNCT
app01-9397	97	5	is	be	AUX
app01-9397	97	6	not	not	PART
app01-9397	97	7	shown	show	VERB
app01-9397	97	8	in	in	ADP
app01-9397	97	9	the	the	DET
app01-9397	97	10	figure	figure	NOUN
app01-9397	97	11	because	because	SCONJ
app01-9397	97	12	this	this	DET
app01-9397	97	13	approach	approach	NOUN
app01-9397	97	14	was	be	AUX
app01-9397	97	15	discarded	discard	VERB
app01-9397	97	16	after	after	ADP
app01-9397	97	17	the	the	DET
app01-9397	97	18	initial	initial	ADJ
app01-9397	97	19	hyper	hyper	ADJ
app01-9397	97	20	parameter	parameter	NOUN
app01-9397	97	21	opti34	opti34	NOUN
app01-9397	97	22	vol	vol	NOUN
app01-9397	97	23	.	.	PUNCT
app01-9397	98	1	42/2023	42/2023	NUM
app01-9397	98	2	ai	ai	AUX
app01-9397	98	3	-	-	PUNCT
app01-9397	98	4	assisted	assist	VERB
app01-9397	98	5	study	study	NOUN
app01-9397	98	6	of	of	ADP
app01-9397	98	7	auxetic	auxetic	ADJ
app01-9397	98	8	structures	structure	NOUN
app01-9397	98	9	mization	mization	NOUN
app01-9397	98	10	stage	stage	NOUN
app01-9397	98	11	due	due	ADP
app01-9397	98	12	to	to	ADP
app01-9397	98	13	extraordinarily	extraordinarily	ADV
app01-9397	98	14	bad	bad	ADJ
app01-9397	98	15	performance	performance	NOUN
app01-9397	98	16	together	together	ADV
app01-9397	98	17	with	with	ADP
app01-9397	98	18	unreasonably	unreasonably	ADV
app01-9397	98	19	large	large	ADJ
app01-9397	98	20	training	training	NOUN
app01-9397	98	21	times	time	NOUN
app01-9397	98	22	.	.	PUNCT
app01-9397	99	1	in	in	ADP
app01-9397	99	2	general	general	ADJ
app01-9397	99	3	,	,	PUNCT
app01-9397	99	4	the	the	DET
app01-9397	99	5	performance	performance	NOUN
app01-9397	99	6	of	of	ADP
app01-9397	99	7	all	all	DET
app01-9397	99	8	models	model	NOUN
app01-9397	99	9	,	,	PUNCT
app01-9397	99	10	with	with	ADP
app01-9397	99	11	the	the	DET
app01-9397	99	12	exception	exception	NOUN
app01-9397	99	13	of	of	ADP
app01-9397	99	14	knnr	knnr	NOUN
app01-9397	99	15	(	(	PUNCT
app01-9397	99	16	sp	sp	NOUN
app01-9397	99	17	)	)	PUNCT
app01-9397	99	18	,	,	PUNCT
app01-9397	99	19	performed	perform	VERB
app01-9397	99	20	even	even	ADV
app01-9397	99	21	better	well	ADV
app01-9397	99	22	of	of	ADP
app01-9397	99	23	the	the	DET
app01-9397	99	24	final	final	ADJ
app01-9397	99	25	test	test	NOUN
app01-9397	99	26	set	set	VERB
app01-9397	99	27	than	than	SCONJ
app01-9397	99	28	estimated	estimate	VERB
app01-9397	99	29	during	during	ADP
app01-9397	99	30	the	the	DET
app01-9397	99	31	model	model	NOUN
app01-9397	99	32	building	building	NOUN
app01-9397	99	33	process	process	NOUN
app01-9397	99	34	,	,	PUNCT
app01-9397	99	35	all	all	DET
app01-9397	99	36	models	model	NOUN
app01-9397	99	37	having	have	VERB
app01-9397	99	38	a	a	DET
app01-9397	99	39	mape	mape	NOUN
app01-9397	99	40	of	of	ADP
app01-9397	99	41	at	at	ADV
app01-9397	99	42	least	least	ADV
app01-9397	99	43	0.573	0.573	NUM
app01-9397	99	44	in	in	ADP
app01-9397	99	45	the	the	DET
app01-9397	99	46	case	case	NOUN
app01-9397	99	47	of	of	ADP
app01-9397	99	48	ann	ann	PROPN
app01-9397	99	49	(	(	PUNCT
app01-9397	99	50	sp	sp	NOUN
app01-9397	99	51	)	)	PUNCT
app01-9397	99	52	.	.	PUNCT
app01-9397	100	1	first	first	ADV
app01-9397	100	2	of	of	ADP
app01-9397	100	3	all	all	PRON
app01-9397	100	4	,	,	PUNCT
app01-9397	100	5	this	this	PRON
app01-9397	100	6	shows	show	VERB
app01-9397	100	7	that	that	SCONJ
app01-9397	100	8	the	the	DET
app01-9397	100	9	efforts	effort	NOUN
app01-9397	100	10	to	to	PART
app01-9397	100	11	provide	provide	VERB
app01-9397	100	12	reliable	reliable	ADJ
app01-9397	100	13	performance	performance	NOUN
app01-9397	100	14	measures	measure	NOUN
app01-9397	100	15	through	through	ADP
app01-9397	100	16	suitable	suitable	ADJ
app01-9397	100	17	data	datum	NOUN
app01-9397	100	18	splitting	splitting	NOUN
app01-9397	100	19	techniques	technique	NOUN
app01-9397	100	20	generally	generally	ADV
app01-9397	100	21	paid	pay	VERB
app01-9397	100	22	off	off	ADP
app01-9397	100	23	since	since	SCONJ
app01-9397	100	24	the	the	DET
app01-9397	100	25	expectations	expectation	NOUN
app01-9397	100	26	from	from	ADP
app01-9397	100	27	the	the	DET
app01-9397	100	28	model	model	NOUN
app01-9397	100	29	building	building	NOUN
app01-9397	100	30	phase	phase	NOUN
app01-9397	100	31	could	could	AUX
app01-9397	100	32	be	be	AUX
app01-9397	100	33	met	meet	VERB
app01-9397	100	34	and	and	CCONJ
app01-9397	100	35	even	even	ADV
app01-9397	100	36	surpassed	surpass	VERB
app01-9397	100	37	in	in	ADP
app01-9397	100	38	the	the	DET
app01-9397	100	39	actual	actual	ADJ
app01-9397	100	40	application	application	NOUN
app01-9397	100	41	scenario	scenario	NOUN
app01-9397	100	42	.	.	PUNCT
app01-9397	101	1	the	the	DET
app01-9397	101	2	overall	overall	ADJ
app01-9397	101	3	improvement	improvement	NOUN
app01-9397	101	4	in	in	ADP
app01-9397	101	5	predictive	predictive	ADJ
app01-9397	101	6	performance	performance	NOUN
app01-9397	101	7	can	can	AUX
app01-9397	101	8	be	be	AUX
app01-9397	101	9	explained	explain	VERB
app01-9397	101	10	by	by	ADP
app01-9397	101	11	the	the	DET
app01-9397	101	12	larger	large	ADJ
app01-9397	101	13	effective	effective	ADJ
app01-9397	101	14	train	train	NOUN
app01-9397	101	15	set	set	NOUN
app01-9397	101	16	.	.	PUNCT
app01-9397	102	1	during	during	ADP
app01-9397	102	2	cross	cross	NOUN
app01-9397	102	3	-	-	ADJ
app01-9397	102	4	validation	validation	ADJ
app01-9397	102	5	,	,	PUNCT
app01-9397	102	6	models	model	NOUN
app01-9397	102	7	were	be	AUX
app01-9397	102	8	trained	train	VERB
app01-9397	102	9	on	on	ADP
app01-9397	102	10	sets	set	NOUN
app01-9397	102	11	consisting	consist	VERB
app01-9397	102	12	of	of	ADP
app01-9397	102	13	86	86	NUM
app01-9397	102	14	stress	stress	NOUN
app01-9397	102	15	-	-	PUNCT
app01-9397	102	16	strain	strain	NOUN
app01-9397	102	17	curves	curve	NOUN
app01-9397	102	18	(	(	PUNCT
app01-9397	102	19	75	75	NUM
app01-9397	102	20	%	%	NOUN
app01-9397	102	21	of	of	ADP
app01-9397	102	22	115	115	NUM
app01-9397	102	23	)	)	PUNCT
app01-9397	102	24	,	,	PUNCT
app01-9397	102	25	whereas	whereas	SCONJ
app01-9397	102	26	all	all	DET
app01-9397	102	27	115	115	NUM
app01-9397	102	28	curves	curve	NOUN
app01-9397	102	29	were	be	AUX
app01-9397	102	30	available	available	ADJ
app01-9397	102	31	to	to	PART
app01-9397	102	32	train	train	VERB
app01-9397	102	33	the	the	DET
app01-9397	102	34	final	final	ADJ
app01-9397	102	35	models	model	NOUN
app01-9397	102	36	.	.	PUNCT
app01-9397	103	1	this	this	PRON
app01-9397	103	2	is	be	AUX
app01-9397	103	3	an	an	DET
app01-9397	103	4	increase	increase	NOUN
app01-9397	103	5	in	in	ADP
app01-9397	103	6	data	datum	NOUN
app01-9397	103	7	amount	amount	NOUN
app01-9397	103	8	of	of	ADP
app01-9397	103	9	about	about	ADP
app01-9397	103	10	28	28	NUM
app01-9397	103	11	%	%	NOUN
app01-9397	103	12	.	.	PUNCT
app01-9397	104	1	the	the	DET
app01-9397	104	2	significant	significant	ADJ
app01-9397	104	3	improvement	improvement	NOUN
app01-9397	104	4	in	in	ADP
app01-9397	104	5	performance	performance	NOUN
app01-9397	104	6	through	through	ADP
app01-9397	104	7	more	more	ADJ
app01-9397	104	8	data	datum	NOUN
app01-9397	104	9	indicates	indicate	VERB
app01-9397	104	10	that	that	SCONJ
app01-9397	104	11	the	the	DET
app01-9397	104	12	amount	amount	NOUN
app01-9397	104	13	of	of	ADP
app01-9397	104	14	training	training	NOUN
app01-9397	104	15	data	datum	NOUN
app01-9397	104	16	is	be	AUX
app01-9397	104	17	at	at	ADP
app01-9397	104	18	least	least	ADJ
app01-9397	104	19	one	one	NUM
app01-9397	104	20	of	of	ADP
app01-9397	104	21	the	the	DET
app01-9397	104	22	limiting	limit	VERB
app01-9397	104	23	factors	factor	NOUN
app01-9397	104	24	regarding	regard	VERB
app01-9397	104	25	predictive	predictive	ADJ
app01-9397	104	26	capability	capability	NOUN
app01-9397	104	27	.	.	PUNCT
app01-9397	105	1	best	good	ADJ
app01-9397	105	2	performance	performance	NOUN
app01-9397	105	3	was	be	AUX
app01-9397	105	4	achieved	achieve	VERB
app01-9397	105	5	by	by	ADP
app01-9397	105	6	feed	feed	NOUN
app01-9397	105	7	-	-	PUNCT
app01-9397	105	8	forward	forward	ADV
app01-9397	105	9	artificial	artificial	ADJ
app01-9397	105	10	neural	neural	ADJ
app01-9397	105	11	networks	network	NOUN
app01-9397	105	12	using	use	VERB
app01-9397	105	13	the	the	DET
app01-9397	105	14	single	single	ADJ
app01-9397	105	15	point	point	NOUN
app01-9397	105	16	approach	approach	NOUN
app01-9397	105	17	with	with	ADP
app01-9397	105	18	a	a	DET
app01-9397	105	19	mape	mape	NOUN
app01-9397	105	20	of	of	ADP
app01-9397	105	21	0.367	0.367	NUM
app01-9397	105	22	±	±	NUM
app01-9397	105	23	0.230	0.230	NUM
app01-9397	105	24	.	.	PUNCT
app01-9397	106	1	the	the	DET
app01-9397	106	2	network	network	NOUN
app01-9397	106	3	used	use	VERB
app01-9397	106	4	the	the	DET
app01-9397	106	5	original	original	ADJ
app01-9397	106	6	input	input	NOUN
app01-9397	106	7	variables	variable	NOUN
app01-9397	106	8	with	with	ADP
app01-9397	106	9	the	the	DET
app01-9397	106	10	exception	exception	NOUN
app01-9397	106	11	of	of	ADP
app01-9397	106	12	angle	angle	NOUN
app01-9397	106	13	being	be	AUX
app01-9397	106	14	replaced	replace	VERB
app01-9397	106	15	with	with	ADP
app01-9397	106	16	gap	gap	NOUN
app01-9397	106	17	,	,	PUNCT
app01-9397	106	18	which	which	PRON
app01-9397	106	19	for	for	ADP
app01-9397	106	20	this	this	DET
app01-9397	106	21	model	model	NOUN
app01-9397	106	22	increased	increase	VERB
app01-9397	106	23	the	the	DET
app01-9397	106	24	predictive	predictive	ADJ
app01-9397	106	25	performance	performance	NOUN
app01-9397	106	26	.	.	PUNCT
app01-9397	107	1	the	the	DET
app01-9397	107	2	final	final	ADJ
app01-9397	107	3	network	network	NOUN
app01-9397	107	4	consist	consist	NOUN
app01-9397	107	5	of	of	ADP
app01-9397	107	6	16	16	NUM
app01-9397	107	7	layers	layer	NOUN
app01-9397	107	8	,	,	PUNCT
app01-9397	107	9	each	each	PRON
app01-9397	107	10	containing	contain	VERB
app01-9397	107	11	18	18	NUM
app01-9397	107	12	neurons	neuron	NOUN
app01-9397	107	13	.	.	PUNCT
app01-9397	108	1	figure	figure	NOUN
app01-9397	108	2	6	6	NUM
app01-9397	108	3	shows	show	VERB
app01-9397	108	4	examples	example	NOUN
app01-9397	108	5	of	of	ADP
app01-9397	108	6	particularly	particularly	ADV
app01-9397	108	7	good	good	ADJ
app01-9397	108	8	and	and	CCONJ
app01-9397	108	9	bad	bad	ADJ
app01-9397	108	10	predictions	prediction	NOUN
app01-9397	108	11	produced	produce	VERB
app01-9397	108	12	by	by	ADP
app01-9397	108	13	the	the	DET
app01-9397	108	14	model	model	NOUN
app01-9397	108	15	.	.	PUNCT
app01-9397	109	1	it	it	PRON
app01-9397	109	2	is	be	AUX
app01-9397	109	3	able	able	ADJ
app01-9397	109	4	to	to	PART
app01-9397	109	5	predict	predict	VERB
app01-9397	109	6	the	the	DET
app01-9397	109	7	general	general	ADJ
app01-9397	109	8	order	order	NOUN
app01-9397	109	9	of	of	ADP
app01-9397	109	10	magnitude	magnitude	NOUN
app01-9397	109	11	correctly	correctly	ADV
app01-9397	109	12	,	,	PUNCT
app01-9397	109	13	however	however	ADV
app01-9397	109	14	,	,	PUNCT
app01-9397	109	15	it	it	PRON
app01-9397	109	16	struggles	struggle	VERB
app01-9397	109	17	with	with	ADP
app01-9397	109	18	predicting	predict	VERB
app01-9397	109	19	the	the	DET
app01-9397	109	20	actual	actual	ADJ
app01-9397	109	21	shape	shape	NOUN
app01-9397	109	22	,	,	PUNCT
app01-9397	109	23	which	which	PRON
app01-9397	109	24	explains	explain	VERB
app01-9397	109	25	the	the	DET
app01-9397	109	26	large	large	ADJ
app01-9397	109	27	standard	standard	ADJ
app01-9397	109	28	deviation	deviation	NOUN
app01-9397	109	29	of	of	ADP
app01-9397	109	30	the	the	DET
app01-9397	109	31	mape	mape	NOUN
app01-9397	109	32	.	.	PUNCT
app01-9397	110	1	this	this	PRON
app01-9397	110	2	is	be	AUX
app01-9397	110	3	not	not	PART
app01-9397	110	4	surprising	surprising	ADJ
app01-9397	110	5	since	since	SCONJ
app01-9397	110	6	different	different	ADJ
app01-9397	110	7	sets	set	NOUN
app01-9397	110	8	of	of	ADP
app01-9397	110	9	geometry	geometry	NOUN
app01-9397	110	10	parameters	parameter	NOUN
app01-9397	110	11	can	can	AUX
app01-9397	110	12	lead	lead	VERB
app01-9397	110	13	to	to	ADP
app01-9397	110	14	stress	stress	NOUN
app01-9397	110	15	-	-	PUNCT
app01-9397	110	16	strain	strain	NOUN
app01-9397	110	17	curves	curve	NOUN
app01-9397	110	18	that	that	PRON
app01-9397	110	19	drastically	drastically	ADV
app01-9397	110	20	vary	vary	VERB
app01-9397	110	21	in	in	ADP
app01-9397	110	22	shape	shape	NOUN
app01-9397	110	23	due	due	ADP
app01-9397	110	24	to	to	ADP
app01-9397	110	25	stability	stability	NOUN
app01-9397	110	26	failure	failure	NOUN
app01-9397	110	27	of	of	ADP
app01-9397	110	28	the	the	DET
app01-9397	110	29	structure	structure	NOUN
app01-9397	110	30	.	.	PUNCT
app01-9397	111	1	in	in	ADP
app01-9397	111	2	the	the	DET
app01-9397	111	3	additional	additional	ADJ
app01-9397	111	4	approach	approach	NOUN
app01-9397	111	5	,	,	PUNCT
app01-9397	111	6	tcns	tcns	PROPN
app01-9397	111	7	were	be	AUX
app01-9397	111	8	employed	employ	VERB
app01-9397	111	9	to	to	PART
app01-9397	111	10	model	model	VERB
app01-9397	111	11	the	the	DET
app01-9397	111	12	stress	stress	NOUN
app01-9397	111	13	-	-	PUNCT
app01-9397	111	14	strain	strain	NOUN
app01-9397	111	15	curves	curve	NOUN
app01-9397	111	16	as	as	ADP
app01-9397	111	17	sequence	sequence	NOUN
app01-9397	111	18	data	datum	NOUN
app01-9397	111	19	.	.	PUNCT
app01-9397	112	1	it	it	PRON
app01-9397	112	2	is	be	AUX
app01-9397	112	3	important	important	ADJ
app01-9397	112	4	to	to	PART
app01-9397	112	5	note	note	VERB
app01-9397	112	6	that	that	SCONJ
app01-9397	112	7	the	the	DET
app01-9397	112	8	tcn	tcn	NOUN
app01-9397	112	9	faced	face	VERB
app01-9397	112	10	a	a	DET
app01-9397	112	11	more	more	ADV
app01-9397	112	12	demanding	demanding	ADJ
app01-9397	112	13	task	task	NOUN
app01-9397	112	14	,	,	PUNCT
app01-9397	112	15	as	as	SCONJ
app01-9397	112	16	it	it	PRON
app01-9397	112	17	aimed	aim	VERB
app01-9397	112	18	to	to	PART
app01-9397	112	19	predict	predict	VERB
app01-9397	112	20	the	the	DET
app01-9397	112	21	full	full	ADJ
app01-9397	112	22	curve	curve	NOUN
app01-9397	112	23	consisting	consist	VERB
app01-9397	112	24	of	of	ADP
app01-9397	112	25	1	1	NUM
app01-9397	112	26	001	001	NUM
app01-9397	112	27	data	datum	NOUN
app01-9397	112	28	points	point	NOUN
app01-9397	112	29	,	,	PUNCT
app01-9397	112	30	which	which	PRON
app01-9397	112	31	is	be	AUX
app01-9397	112	32	inherently	inherently	ADV
app01-9397	112	33	more	more	ADV
app01-9397	112	34	complex	complex	ADJ
app01-9397	112	35	compared	compare	VERB
app01-9397	112	36	to	to	ADP
app01-9397	112	37	what	what	PRON
app01-9397	112	38	the	the	DET
app01-9397	112	39	other	other	ADJ
app01-9397	112	40	models	model	NOUN
app01-9397	112	41	were	be	AUX
app01-9397	112	42	required	require	VERB
app01-9397	112	43	to	to	PART
app01-9397	112	44	accomplish	accomplish	VERB
app01-9397	112	45	.	.	PUNCT
app01-9397	113	1	consequently	consequently	ADV
app01-9397	113	2	,	,	PUNCT
app01-9397	113	3	the	the	DET
app01-9397	113	4	tcn	tcn	NOUN
app01-9397	113	5	did	do	AUX
app01-9397	113	6	not	not	PART
app01-9397	113	7	prove	prove	VERB
app01-9397	113	8	to	to	PART
app01-9397	113	9	be	be	AUX
app01-9397	113	10	as	as	ADV
app01-9397	113	11	adept	adept	ADJ
app01-9397	113	12	for	for	ADP
app01-9397	113	13	this	this	DET
app01-9397	113	14	specific	specific	ADJ
app01-9397	113	15	problem	problem	NOUN
app01-9397	113	16	context	context	NOUN
app01-9397	113	17	as	as	SCONJ
app01-9397	113	18	was	be	AUX
app01-9397	113	19	hoped	hope	VERB
app01-9397	113	20	.	.	PUNCT
app01-9397	114	1	however	however	ADV
app01-9397	114	2	,	,	PUNCT
app01-9397	114	3	exploring	explore	VERB
app01-9397	114	4	this	this	DET
app01-9397	114	5	method	method	NOUN
app01-9397	114	6	served	serve	VERB
app01-9397	114	7	to	to	PART
app01-9397	114	8	reinforce	reinforce	VERB
app01-9397	114	9	the	the	DET
app01-9397	114	10	decisions	decision	NOUN
app01-9397	114	11	made	make	VERB
app01-9397	114	12	in	in	ADP
app01-9397	114	13	the	the	DET
app01-9397	114	14	main	main	ADJ
app01-9397	114	15	study	study	NOUN
app01-9397	114	16	,	,	PUNCT
app01-9397	114	17	and	and	CCONJ
app01-9397	114	18	highlighted	highlight	VERB
app01-9397	114	19	the	the	DET
app01-9397	114	20	importance	importance	NOUN
app01-9397	114	21	of	of	ADP
app01-9397	114	22	model	model	NOUN
app01-9397	114	23	selection	selection	NOUN
app01-9397	114	24	in	in	ADP
app01-9397	114	25	relation	relation	NOUN
app01-9397	114	26	to	to	ADP
app01-9397	114	27	the	the	DET
app01-9397	114	28	complexity	complexity	NOUN
app01-9397	114	29	of	of	ADP
app01-9397	114	30	the	the	DET
app01-9397	114	31	task	task	NOUN
app01-9397	114	32	.	.	PUNCT
app01-9397	115	1	4	4	NUM
app01-9397	115	2	.	.	X
app01-9397	115	3	conclusion	conclusion	NOUN
app01-9397	115	4	and	and	CCONJ
app01-9397	115	5	future	future	ADJ
app01-9397	115	6	work	work	NOUN
app01-9397	115	7	a	a	DET
app01-9397	115	8	model	model	NOUN
app01-9397	115	9	was	be	AUX
app01-9397	115	10	trained	train	VERB
app01-9397	115	11	that	that	PRON
app01-9397	115	12	can	can	AUX
app01-9397	115	13	predict	predict	VERB
app01-9397	115	14	stress	stress	NOUN
app01-9397	115	15	-	-	PUNCT
app01-9397	115	16	strain	strain	NOUN
app01-9397	115	17	curves	curve	NOUN
app01-9397	115	18	for	for	ADP
app01-9397	115	19	a	a	DET
app01-9397	115	20	re	re	ADJ
app01-9397	115	21	-	-	ADJ
app01-9397	115	22	entrant	entrant	ADJ
app01-9397	115	23	auxetic	auxetic	ADJ
app01-9397	115	24	structure	structure	NOUN
app01-9397	115	25	,	,	PUNCT
app01-9397	115	26	see	see	VERB
app01-9397	115	27	figure	figure	NOUN
app01-9397	115	28	1	1	NUM
app01-9397	115	29	,	,	PUNCT
app01-9397	115	30	based	base	VERB
app01-9397	115	31	only	only	ADV
app01-9397	115	32	on	on	ADP
app01-9397	115	33	geometry	geometry	NOUN
app01-9397	115	34	-	-	PUNCT
app01-9397	115	35	describing	describe	VERB
app01-9397	115	36	parameters	parameter	NOUN
app01-9397	115	37	,	,	PUNCT
app01-9397	115	38	achieving	achieve	VERB
app01-9397	115	39	a	a	DET
app01-9397	115	40	mean	mean	ADJ
app01-9397	115	41	absolute	absolute	ADJ
app01-9397	115	42	percentage	percentage	NOUN
app01-9397	115	43	error	error	NOUN
app01-9397	115	44	of	of	ADP
app01-9397	115	45	0.367±0.230	0.367±0.230	PRON
app01-9397	115	46	.	.	PUNCT
app01-9397	116	1	even	even	ADV
app01-9397	116	2	though	though	SCONJ
app01-9397	116	3	at	at	ADP
app01-9397	116	4	this	this	DET
app01-9397	116	5	point	point	NOUN
app01-9397	116	6	the	the	DET
app01-9397	116	7	model	model	NOUN
app01-9397	116	8	can	can	AUX
app01-9397	116	9	not	not	PART
app01-9397	116	10	replace	replace	VERB
app01-9397	116	11	conventional	conventional	ADJ
app01-9397	116	12	finite	finite	ADJ
app01-9397	116	13	element	element	NOUN
app01-9397	116	14	simulations	simulation	NOUN
app01-9397	116	15	,	,	PUNCT
app01-9397	116	16	it	it	PRON
app01-9397	116	17	allows	allow	VERB
app01-9397	116	18	to	to	PART
app01-9397	116	19	highly	highly	ADV
app01-9397	116	20	reduce	reduce	VERB
app01-9397	116	21	the	the	DET
app01-9397	116	22	amount	amount	NOUN
app01-9397	116	23	of	of	ADP
app01-9397	116	24	simulations	simulation	NOUN
app01-9397	116	25	that	that	PRON
app01-9397	116	26	actually	actually	ADV
app01-9397	116	27	figure	figure	VERB
app01-9397	116	28	6	6	NUM
app01-9397	116	29	.	.	PUNCT
app01-9397	116	30	particularly	particularly	ADV
app01-9397	116	31	good	good	ADJ
app01-9397	116	32	(	(	PUNCT
app01-9397	116	33	top	top	ADJ
app01-9397	116	34	)	)	PUNCT
app01-9397	116	35	and	and	CCONJ
app01-9397	116	36	particularly	particularly	ADV
app01-9397	116	37	bad	bad	ADJ
app01-9397	116	38	(	(	PUNCT
app01-9397	116	39	bottom	bottom	ADJ
app01-9397	116	40	)	)	PUNCT
app01-9397	116	41	predictions	prediction	NOUN
app01-9397	116	42	produced	produce	VERB
app01-9397	116	43	by	by	ADP
app01-9397	116	44	the	the	DET
app01-9397	116	45	final	final	ADJ
app01-9397	116	46	optimized	optimize	VERB
app01-9397	116	47	artificial	artificial	ADJ
app01-9397	116	48	neural	neural	ADJ
app01-9397	116	49	network	network	NOUN
app01-9397	116	50	using	use	VERB
app01-9397	116	51	the	the	DET
app01-9397	116	52	sp	sp	NOUN
app01-9397	116	53	approach	approach	NOUN
app01-9397	116	54	.	.	PUNCT
app01-9397	117	1	need	need	VERB
app01-9397	117	2	to	to	PART
app01-9397	117	3	be	be	AUX
app01-9397	117	4	carried	carry	VERB
app01-9397	117	5	out	out	ADP
app01-9397	117	6	by	by	ADP
app01-9397	117	7	providing	provide	VERB
app01-9397	117	8	a	a	DET
app01-9397	117	9	rough	rough	ADJ
app01-9397	117	10	estimate	estimate	NOUN
app01-9397	117	11	of	of	ADP
app01-9397	117	12	the	the	DET
app01-9397	117	13	expected	expect	VERB
app01-9397	117	14	curve	curve	NOUN
app01-9397	117	15	within	within	ADP
app01-9397	117	16	a	a	DET
app01-9397	117	17	fraction	fraction	NOUN
app01-9397	117	18	of	of	ADP
app01-9397	117	19	a	a	DET
app01-9397	117	20	second	second	ADJ
app01-9397	117	21	–	–	PUNCT
app01-9397	117	22	compared	compare	VERB
app01-9397	117	23	to	to	ADP
app01-9397	117	24	up	up	ADP
app01-9397	117	25	to	to	PART
app01-9397	117	26	48	48	NUM
app01-9397	117	27	h	h	NOUN
app01-9397	117	28	for	for	ADP
app01-9397	117	29	the	the	DET
app01-9397	117	30	classical	classical	ADJ
app01-9397	117	31	simulation	simulation	NOUN
app01-9397	117	32	.	.	PUNCT
app01-9397	118	1	furthermore	furthermore	ADV
app01-9397	118	2	,	,	PUNCT
app01-9397	118	3	it	it	PRON
app01-9397	118	4	was	be	AUX
app01-9397	118	5	shown	show	VERB
app01-9397	118	6	that	that	SCONJ
app01-9397	118	7	feed	feed	NOUN
app01-9397	118	8	-	-	PUNCT
app01-9397	118	9	forward	forward	ADV
app01-9397	118	10	artificial	artificial	ADJ
app01-9397	118	11	neural	neural	ADJ
app01-9397	118	12	networks	network	NOUN
app01-9397	118	13	are	be	AUX
app01-9397	118	14	best	well	ADV
app01-9397	118	15	suited	suit	VERB
app01-9397	118	16	for	for	ADP
app01-9397	118	17	the	the	DET
app01-9397	118	18	task	task	NOUN
app01-9397	118	19	at	at	ADP
app01-9397	118	20	hand	hand	NOUN
app01-9397	118	21	,	,	PUNCT
app01-9397	118	22	significantly	significantly	ADV
app01-9397	118	23	outperforming	outperform	VERB
app01-9397	118	24	all	all	DET
app01-9397	118	25	other	other	ADJ
app01-9397	118	26	studied	studied	ADJ
app01-9397	118	27	options	option	NOUN
app01-9397	118	28	.	.	PUNCT
app01-9397	119	1	additionally	additionally	ADV
app01-9397	119	2	,	,	PUNCT
app01-9397	119	3	models	model	NOUN
app01-9397	119	4	like	like	ADP
app01-9397	119	5	support	support	NOUN
app01-9397	119	6	vector	vector	NOUN
app01-9397	119	7	regression	regression	NOUN
app01-9397	119	8	and	and	CCONJ
app01-9397	119	9	temporal	temporal	ADJ
app01-9397	119	10	convolutional	convolutional	ADJ
app01-9397	119	11	networks	network	NOUN
app01-9397	119	12	were	be	AUX
app01-9397	119	13	found	find	VERB
app01-9397	119	14	to	to	PART
app01-9397	119	15	be	be	AUX
app01-9397	119	16	particularly	particularly	ADV
app01-9397	119	17	unsuitable	unsuitable	ADJ
app01-9397	119	18	for	for	ADP
app01-9397	119	19	the	the	DET
app01-9397	119	20	studied	study	VERB
app01-9397	119	21	problem	problem	NOUN
app01-9397	119	22	.	.	PUNCT
app01-9397	120	1	since	since	SCONJ
app01-9397	120	2	an	an	DET
app01-9397	120	3	increase	increase	NOUN
app01-9397	120	4	in	in	ADP
app01-9397	120	5	training	training	NOUN
app01-9397	120	6	data	datum	NOUN
app01-9397	120	7	volume	volume	NOUN
app01-9397	120	8	enhanced	enhance	VERB
app01-9397	120	9	the	the	DET
app01-9397	120	10	predictive	predictive	ADJ
app01-9397	120	11	performance	performance	NOUN
app01-9397	120	12	in	in	ADP
app01-9397	120	13	this	this	DET
app01-9397	120	14	study	study	NOUN
app01-9397	120	15	,	,	PUNCT
app01-9397	120	16	further	further	ADJ
app01-9397	120	17	improvements	improvement	NOUN
app01-9397	120	18	to	to	ADP
app01-9397	120	19	the	the	DET
app01-9397	120	20	models	model	NOUN
app01-9397	120	21	can	can	AUX
app01-9397	120	22	be	be	AUX
app01-9397	120	23	expected	expect	VERB
app01-9397	120	24	by	by	ADP
app01-9397	120	25	incorporating	incorporate	VERB
app01-9397	120	26	more	more	ADJ
app01-9397	120	27	and	and	CCONJ
app01-9397	120	28	more	more	ADV
app01-9397	120	29	simulated	simulated	ADJ
app01-9397	120	30	curves	curve	NOUN
app01-9397	120	31	into	into	ADP
app01-9397	120	32	the	the	DET
app01-9397	120	33	data	data	NOUN
app01-9397	120	34	set	set	VERB
app01-9397	120	35	.	.	PUNCT
app01-9397	121	1	apart	apart	ADV
app01-9397	121	2	from	from	ADP
app01-9397	121	3	a	a	DET
app01-9397	121	4	simple	simple	ADJ
app01-9397	121	5	increase	increase	NOUN
app01-9397	121	6	in	in	ADP
app01-9397	121	7	data	datum	NOUN
app01-9397	121	8	volume	volume	NOUN
app01-9397	121	9	,	,	PUNCT
app01-9397	121	10	additional	additional	ADJ
app01-9397	121	11	models	model	NOUN
app01-9397	121	12	are	be	AUX
app01-9397	121	13	planned	plan	VERB
app01-9397	121	14	to	to	PART
app01-9397	121	15	be	be	AUX
app01-9397	121	16	trained	train	VERB
app01-9397	121	17	predicting	predict	VERB
app01-9397	121	18	effective	effective	ADJ
app01-9397	121	19	quantities	quantity	NOUN
app01-9397	121	20	like	like	ADP
app01-9397	121	21	the	the	DET
app01-9397	121	22	absorbed	absorb	VERB
app01-9397	121	23	energy	energy	NOUN
app01-9397	121	24	during	during	ADP
app01-9397	121	25	the	the	DET
app01-9397	121	26	compressive	compressive	ADJ
app01-9397	121	27	loading	loading	NOUN
app01-9397	121	28	.	.	PUNCT
app01-9397	122	1	since	since	SCONJ
app01-9397	122	2	predicting	predict	VERB
app01-9397	122	3	one	one	NUM
app01-9397	122	4	quantity	quantity	NOUN
app01-9397	122	5	per	per	ADP
app01-9397	122	6	geometry	geometry	NOUN
app01-9397	122	7	parameter	parameter	NOUN
app01-9397	122	8	combination	combination	NOUN
app01-9397	122	9	instead	instead	ADV
app01-9397	122	10	of	of	ADP
app01-9397	122	11	a	a	DET
app01-9397	122	12	whole	whole	ADJ
app01-9397	122	13	curve	curve	NOUN
app01-9397	122	14	reduces	reduce	VERB
app01-9397	122	15	the	the	DET
app01-9397	122	16	complexity	complexity	NOUN
app01-9397	122	17	of	of	ADP
app01-9397	122	18	the	the	DET
app01-9397	122	19	problem	problem	NOUN
app01-9397	122	20	,	,	PUNCT
app01-9397	122	21	better	well	ADJ
app01-9397	122	22	results	result	NOUN
app01-9397	122	23	are	be	AUX
app01-9397	122	24	to	to	PART
app01-9397	122	25	be	be	AUX
app01-9397	122	26	expected	expect	VERB
app01-9397	122	27	.	.	PUNCT
app01-9397	123	1	also	also	ADV
app01-9397	123	2	,	,	PUNCT
app01-9397	123	3	since	since	SCONJ
app01-9397	123	4	in	in	ADP
app01-9397	123	5	this	this	DET
app01-9397	123	6	case	case	NOUN
app01-9397	123	7	no	no	DET
app01-9397	123	8	curve	curve	NOUN
app01-9397	123	9	shape	shape	NOUN
app01-9397	123	10	needs	need	VERB
app01-9397	123	11	to	to	PART
app01-9397	123	12	be	be	AUX
app01-9397	123	13	matched	match	VERB
app01-9397	123	14	,	,	PUNCT
app01-9397	123	15	the	the	DET
app01-9397	123	16	spread	spread	NOUN
app01-9397	123	17	in	in	ADP
app01-9397	123	18	performance	performance	NOUN
app01-9397	123	19	is	be	AUX
app01-9397	123	20	expected	expect	VERB
app01-9397	123	21	to	to	PART
app01-9397	123	22	be	be	AUX
app01-9397	123	23	reduced	reduce	VERB
app01-9397	123	24	.	.	PUNCT
app01-9397	124	1	given	give	VERB
app01-9397	124	2	the	the	DET
app01-9397	124	3	expected	expect	VERB
app01-9397	124	4	possible	possible	ADJ
app01-9397	124	5	frequency	frequency	NOUN
app01-9397	124	6	with	with	ADP
app01-9397	124	7	which	which	PRON
app01-9397	124	8	predictions	prediction	NOUN
app01-9397	124	9	can	can	AUX
app01-9397	124	10	be	be	AUX
app01-9397	124	11	made	make	VERB
app01-9397	124	12	,	,	PUNCT
app01-9397	124	13	an	an	DET
app01-9397	124	14	extension	extension	NOUN
app01-9397	124	15	of	of	ADP
app01-9397	124	16	the	the	DET
app01-9397	124	17	framework	framework	NOUN
app01-9397	124	18	is	be	AUX
app01-9397	124	19	conceiv35	conceiv35	PROPN
app01-9397	124	20	s.	s.	PROPN
app01-9397	124	21	grednev	grednev	PROPN
app01-9397	124	22	,	,	PUNCT
app01-9397	124	23	h.	h.	PROPN
app01-9397	124	24	s.	s.	PROPN
app01-9397	124	25	steude	steude	PROPN
app01-9397	124	26	,	,	PUNCT
app01-9397	124	27	s.	s.	PROPN
app01-9397	124	28	bronder	bronder	PROPN
app01-9397	124	29	et	et	PROPN
app01-9397	124	30	al	al	PROPN
app01-9397	124	31	.	.	PROPN
app01-9397	124	32	acta	acta	PROPN
app01-9397	124	33	polytechnica	polytechnica	PROPN
app01-9397	124	34	ctu	ctu	NOUN
app01-9397	124	35	proceedings	proceeding	NOUN
app01-9397	124	36	able	able	ADJ
app01-9397	124	37	where	where	SCONJ
app01-9397	124	38	the	the	DET
app01-9397	124	39	user	user	NOUN
app01-9397	124	40	chooses	choose	VERB
app01-9397	124	41	a	a	DET
app01-9397	124	42	desired	desire	VERB
app01-9397	124	43	specific	specific	ADJ
app01-9397	124	44	energy	energy	NOUN
app01-9397	124	45	absorption	absorption	NOUN
app01-9397	124	46	for	for	ADP
app01-9397	124	47	a	a	DET
app01-9397	124	48	particular	particular	ADJ
app01-9397	124	49	application	application	NOUN
app01-9397	124	50	and	and	CCONJ
app01-9397	124	51	the	the	DET
app01-9397	124	52	model	model	NOUN
app01-9397	124	53	provides	provide	VERB
app01-9397	124	54	the	the	DET
app01-9397	124	55	corresponding	corresponding	ADJ
app01-9397	124	56	geometry	geometry	NOUN
app01-9397	124	57	.	.	PUNCT
app01-9397	125	1	references	reference	NOUN
app01-9397	125	2	[	[	X
app01-9397	125	3	1	1	NUM
app01-9397	125	4	]	]	PUNCT
app01-9397	125	5	a.	a.	NOUN
app01-9397	125	6	bezazi	bezazi	PROPN
app01-9397	125	7	,	,	PUNCT
app01-9397	125	8	f.	f.	PROPN
app01-9397	125	9	scarpa	scarpa	PROPN
app01-9397	125	10	.	.	PUNCT
app01-9397	126	1	mechanical	mechanical	ADJ
app01-9397	126	2	behaviour	behaviour	NOUN
app01-9397	126	3	of	of	ADP
app01-9397	126	4	conventional	conventional	ADJ
app01-9397	126	5	and	and	CCONJ
app01-9397	126	6	negative	negative	ADJ
app01-9397	126	7	poisson	poisson	NOUN
app01-9397	126	8	’s	’s	PART
app01-9397	126	9	ratio	ratio	NOUN
app01-9397	126	10	thermoplastic	thermoplastic	ADJ
app01-9397	126	11	polyurethane	polyurethane	NOUN
app01-9397	126	12	foams	foam	NOUN
app01-9397	126	13	under	under	ADP
app01-9397	126	14	compressive	compressive	ADJ
app01-9397	126	15	cyclic	cyclic	ADJ
app01-9397	126	16	loading	loading	NOUN
app01-9397	126	17	.	.	PUNCT
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app01-9397	127	2	journal	journal	NOUN
app01-9397	127	3	of	of	ADP
app01-9397	127	4	fatigue	fatigue	NOUN
app01-9397	127	5	29(5):922–930	29(5):922–930	PROPN
app01-9397	127	6	,	,	PUNCT
app01-9397	127	7	2007	2007	NUM
app01-9397	127	8	.	.	PUNCT
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app01-9397	129	2	2	2	X
app01-9397	129	3	]	]	PUNCT
app01-9397	129	4	f.	f.	PROPN
app01-9397	129	5	scarpa	scarpa	PROPN
app01-9397	129	6	,	,	PUNCT
app01-9397	129	7	p.	p.	PROPN
app01-9397	129	8	pastorino	pastorino	PROPN
app01-9397	129	9	,	,	PUNCT
app01-9397	129	10	a.	a.	PROPN
app01-9397	129	11	garelli	garelli	PROPN
app01-9397	129	12	,	,	PUNCT
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app01-9397	129	14	al	al	PROPN
app01-9397	129	15	.	.	PUNCT
app01-9397	129	16	auxetic	auxetic	PROPN
app01-9397	129	17	compliant	compliant	ADJ
app01-9397	129	18	flexible	flexible	ADJ
app01-9397	129	19	pu	pu	PROPN
app01-9397	129	20	foams	foam	NOUN
app01-9397	129	21	:	:	PUNCT
app01-9397	129	22	static	static	ADJ
app01-9397	129	23	and	and	CCONJ
app01-9397	129	24	dynamic	dynamic	ADJ
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app01-9397	130	1	physica	physica	PROPN
app01-9397	130	2	status	status	PROPN
app01-9397	130	3	solidi	solidi	PROPN
app01-9397	130	4	(	(	PUNCT
app01-9397	130	5	b	b	NOUN
app01-9397	130	6	)	)	PUNCT
app01-9397	130	7	basic	basic	ADJ
app01-9397	130	8	research	research	NOUN
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app01-9397	130	10	,	,	PUNCT
app01-9397	130	11	2005	2005	NUM
app01-9397	130	12	.	.	PUNCT
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app01-9397	132	2	3	3	NUM
app01-9397	132	3	]	]	PUNCT
app01-9397	132	4	m.	m.	NOUN
app01-9397	132	5	mozaffar	mozaffar	PROPN
app01-9397	132	6	,	,	PUNCT
app01-9397	132	7	r.	r.	PROPN
app01-9397	132	8	bostanabad	bostanabad	PROPN
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app01-9397	132	14	al	al	PROPN
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app01-9397	133	2	learning	learning	NOUN
app01-9397	133	3	predicts	predict	VERB
app01-9397	133	4	path	path	NOUN
app01-9397	133	5	-	-	PUNCT
app01-9397	133	6	dependent	dependent	ADJ
app01-9397	133	7	plasticity	plasticity	NOUN
app01-9397	133	8	.	.	PUNCT
app01-9397	134	1	proceedings	proceeding	NOUN
app01-9397	134	2	of	of	ADP
app01-9397	134	3	the	the	DET
app01-9397	134	4	national	national	PROPN
app01-9397	134	5	academy	academy	PROPN
app01-9397	134	6	of	of	ADP
app01-9397	134	7	sciences	sciences	PROPN
app01-9397	134	8	of	of	ADP
app01-9397	134	9	the	the	DET
app01-9397	134	10	united	united	PROPN
app01-9397	134	11	states	states	PROPN
app01-9397	134	12	of	of	ADP
app01-9397	134	13	america	america	PROPN
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app01-9397	134	15	,	,	PUNCT
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app01-9397	134	17	.	.	PUNCT
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app01-9397	136	2	4	4	X
app01-9397	136	3	]	]	PUNCT
app01-9397	136	4	s.	s.	PROPN
app01-9397	136	5	bronder	bronder	PROPN
app01-9397	136	6	,	,	PUNCT
app01-9397	136	7	s.	s.	PROPN
app01-9397	136	8	diebels	diebels	PROPN
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app01-9397	136	10	a.	a.	PROPN
app01-9397	136	11	jung	jung	PROPN
app01-9397	136	12	.	.	PUNCT
app01-9397	137	1	neural	neural	ADJ
app01-9397	137	2	networks	network	NOUN
app01-9397	137	3	for	for	ADP
app01-9397	137	4	structural	structural	ADJ
app01-9397	137	5	optimisation	optimisation	NOUN
app01-9397	137	6	of	of	ADP
app01-9397	137	7	mechanical	mechanical	ADJ
app01-9397	137	8	metamaterials	metamaterial	NOUN
app01-9397	137	9	.	.	PUNCT
app01-9397	138	1	pamm	pamm	PROPN
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app01-9397	138	3	,	,	PUNCT
app01-9397	138	4	2020	2020	NUM
app01-9397	138	5	.	.	PUNCT
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app01-9397	140	2	5	5	X
app01-9397	140	3	]	]	PUNCT
app01-9397	140	4	s.	s.	PROPN
app01-9397	140	5	bronder	bronder	PROPN
app01-9397	140	6	,	,	PUNCT
app01-9397	140	7	f.	f.	PROPN
app01-9397	140	8	herter	herter	PROPN
app01-9397	140	9	,	,	PUNCT
app01-9397	140	10	a.	a.	NOUN
app01-9397	140	11	röhrig	röhrig	PROPN
app01-9397	140	12	,	,	PUNCT
app01-9397	140	13	et	et	PROPN
app01-9397	140	14	al	al	PROPN
app01-9397	140	15	.	.	PUNCT
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app01-9397	141	2	study	study	NOUN
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app01-9397	141	7	-	-	NOUN
app01-9397	141	8	entrant	entrant	ADJ
app01-9397	141	9	auxetics	auxetic	NOUN
app01-9397	141	10	.	.	PUNCT
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app01-9397	142	2	engineering	engineering	NOUN
app01-9397	142	3	materials	material	NOUN
app01-9397	142	4	24(1):2100816	24(1):2100816	NUM
app01-9397	142	5	,	,	PUNCT
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app01-9397	142	7	.	.	PUNCT
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app01-9397	144	3	]	]	PUNCT
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app01-9397	144	5	rosenblatt	rosenblatt	PROPN
app01-9397	144	6	.	.	PUNCT
app01-9397	145	1	the	the	DET
app01-9397	145	2	perceptron	perceptron	PROPN
app01-9397	145	3	:	:	PUNCT
app01-9397	145	4	a	a	DET
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app01-9397	145	6	model	model	NOUN
app01-9397	145	7	for	for	ADP
app01-9397	145	8	information	information	NOUN
app01-9397	145	9	storage	storage	NOUN
app01-9397	145	10	and	and	CCONJ
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app01-9397	145	13	the	the	DET
app01-9397	145	14	brain	brain	NOUN
app01-9397	145	15	.	.	PUNCT
app01-9397	146	1	psychological	psychological	ADJ
app01-9397	146	2	review	review	NOUN
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app01-9397	146	4	,	,	PUNCT
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app01-9397	146	6	.	.	PUNCT
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app01-9397	147	3	7	7	X
app01-9397	147	4	]	]	X
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app01-9397	147	6	patterson	patterson	PROPN
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app01-9397	148	2	learning	learning	NOUN
app01-9397	148	3	:	:	PUNCT
app01-9397	148	4	a	a	DET
app01-9397	148	5	practitioner	practitioner	NOUN
app01-9397	148	6	’s	’s	PART
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app01-9397	149	2	,	,	PUNCT
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app01-9397	150	2	8	8	NUM
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app01-9397	150	5	kuhn	kuhn	PROPN
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app01-9397	150	7	k.	k.	PROPN
app01-9397	150	8	johnson	johnson	PROPN
app01-9397	150	9	.	.	PUNCT
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app01-9397	151	2	predictive	predictive	ADJ
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app01-9397	151	4	.	.	PUNCT
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app01-9397	153	11	hastie	hastie	PROPN
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app01-9397	159	4	,	,	PUNCT
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app01-9397	163	29	data	datum	NOUN
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app01-9397	163	48	4	4	NUM
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