Predicting the soliton dynamics and system parameters in optical fiber couplers

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

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Maziar Raissi, Alireza Yazdani, George Em Karniadakis · Science · 2020 · 2K 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

When and why PINNs fail to train: A neural tangent kernel perspective

Sifan Wang, Xinling Yu, Paris Perdikaris · Journal of Computational Physics · 2021 · 1.2K citations · Full text

Computing, Computer Science, Machine Learning +4

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

Liu Yang, Xuhui Meng, George Em Karniadakis · Journal of Computational Physics · 2020 · 1.1K citations · Full text

Natural Sciences, Mathematics, Physics +14