标题/作者	年份
Physics-informed machine learning GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang Nature Reviews Physics 3 (6), 422-440	2021
Scientific machine learning through physics–informed neural networks: Where we are and what’s next S Cuomo, VS Di Cola, F Giampaolo, G Rozza, M Raissi, F Piccialli Journal of Scientific Computing 92 (3), 88	2022
Physics-informed neural networks (PINNs) for fluid mechanics: A review S Cai, Z Mao, Z Wang, M Yin, GE Karniadakis Acta Mechanica Sinica 37 (12), 1727-1738	2021
Physics-informed neural networks for heat transfer problems S Cai, Z Wang, S Wang, P Perdikaris, GE Karniadakis Journal of Heat Transfer 143 (6), 060801	2021
Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations AD Jagtap, GE Karniadakis Communications in Computational Physics 28 (5)	2020
hp-VPINNs: Variational physics-informed neural networks with domain decomposition E Kharazmi, Z Zhang, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 374, 113547	2021
Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks AD Jagtap, K Kawaguchi, G Em Karniadakis Proceedings of the Royal Society A 476 (2239), 20200334	2020
Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 42 (2), 981-1022	2022
Parallel physics-informed neural networks via domain decomposition K Shukla, AD Jagtap, GE Karniadakis Journal of Computational Physics 447, 110683	2021
Physics-informed deep learning for computational elastodynamics without labeled data C Rao, H Sun, Y Liu Journal of Engineering Mechanics 147 (8), 04021043	2021
Estimates on the generalization error of physics-informed neural networks for approximating PDEs S Mishra, R Molinaro IMA Journal of Numerical Analysis 43 (1), 1-43	2023
Three ways to solve partial differential equations with neural networks—A review J Blechschmidt, OG Ernst GAMM‐Mitteilungen 44 (2), e202100006	2021
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries A Kashefi, D Rempe, LJ Guibas Physics of Fluids 33 (2)	2021
Physics‐informed neural networks (PINNs) for wave propagation and full waveform inversions M Rasht‐Behesht, C Huber, K Shukla, GE Karniadakis Journal of Geophysical Research: Solid Earth 127 (5), e2021JB023120	2022
Data-driven femtosecond optical soliton excitations and parameters discovery of the high-order NLSE using the PINN Y Fang, GZ Wu, YY Wang, CQ Dai Nonlinear Dynamics 105 (1), 603-616	2021
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture SA Niaki, E Haghighat, T Campbell, A Poursartip, R Vaziri Computer Methods in Applied Mechanics and Engineering 384, 113959	2021
Physics-informed neural networks for inverse problems in supersonic flows AD Jagtap, Z Mao, N Adams, GE Karniadakis Journal of Computational Physics 466, 111402	2022
Analyses of internal structures and defects in materials using physics-informed neural networks E Zhang, M Dao, GE Karniadakis, S Suresh Science advances 8 (7), eabk0644	2022
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems H Gao, MJ Zahr, JX Wang Computer Methods in Applied Mechanics and Engineering 390, 114502	2022
Self-adaptive loss balanced physics-informed neural networks Z Xiang, W Peng, X Liu, W Yao Neurocomputing 496, 11-34	2022

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