A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018 · 16.9K citations · Full text

Natural Sciences, Computer Science, Physics +16

On the limited memory BFGS method for large scale optimization

Jorge Nocedal · Mathematical Programming · 1989 · 8.5K citations

Computing, Computer Engineering, Computer Science +12

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo et al. · Journal of Scientific Computing · 2022 · 2.3K citations · Full text

Computing, Computer Science, Physics +16

Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

Sifan Wang, Yujun Teng, Paris Perdikaris · SIAM Journal on Scientific Computing · 2021 · 1.6K citations

Natural Sciences, Computer Science, Physics +14

Exponential Time Differencing for Stiff Systems

Stephen M. Cox, P. C. Matthews · Journal of Computational Physics · 2002 · 1.3K citations

Natural Sciences, Mathematics, Mechanical Engineering +9