id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cord-159425-fgbruo9l	Paticchio, Alessandro	Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread	2020-10-10		.txt	text/plain	2531	119	51	Starting from random z 0 and θ, a solutionẑ(t) is generated, then a gradient descent optimizer adjusts z 0 and θ in order to minimize the loss function: Figure 1 : Semi-supervised neural network architecture. Statistical methods in medical research Solving differential equations using neural network solution bundles Modelling the covid-19 epidemic and implementation of population-wide interventions in italy Multiple epidemic wave model of the covid-19 pandemic: Modeling study The first 100 days: Modeling the evolution of the covid-19 pandemic Early dynamics of transmission and control of covid-19: a mathematical modelling study.	cache/cord-159425-fgbruo9l.txt	txt/cord-159425-fgbruo9l.txt
