wbPINN: Weight balanced physics-informed neural networks for multi-objective learning

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

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Roberto Cipolla, Yarin Gal, Alex Kendall · 2018 · 2.8K citations · Full text

Computing, Computer Science, Computer Vision +14

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

Physics-informed neural networks for high-speed flows

Zhiping Mao, Ameya D. Jagtap, George Em Karniadakis · Computer Methods in Applied Mechanics and Engineering · 2019 · 1.2K citations

Computing, Computational Engineering, 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