id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-16292	Antonion, Klapa; Wang, Xiao; Raissi, Maziar; Joshie, Laurn	Machine Learning Through Physics–Informed Neural Networks: Progress and Challenges	2024	4	.pdf	application/pdf	3424	141	29	Additionally, the envisioned potential of deep neural networks to construct interpretable hybrid Earth system models for Earth and climate sciences further underscores their significance[18]. Some researchers[20] introduced a comprehensive taxonomy, termed 'informed deep learning,' organizing it into three core conceptual stages: delineating the type of deep neural network used, representing physical knowledge, and integrating this information.	cache/ajst-16292.pdf	txt/ajst-16292.txt