Least-square finite difference-based physics-informed neural network for steady incompressible flows

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

Navier Stokes Equations: Theory and Numerical Analysis

Roger Témam, Alexandre J. Chorin · Journal of Applied Mechanics · 1978 · 4K citations

Natural Sciences, Mathematics, Physics +17

Automatic differentiation in machine learning: a survey

Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Radul et al. · Maynooth University ePrints and eTheses Archive (Maynooth University) · 2015 · 2.1K citations · Full text

Computing, Computer Science, Derivatives +10

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

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis · Journal of Computational Physics · 2019 · 1K citations · Full text

Computing, Computer Science, Physics +10