Respecting causality for training physics-informed neural networks

2024/02/07 by Sifan Wang, Shyam Sankaran, Paris Perdikaris · 32 citations
Physics and Astronomy · Engineering · Computer Science · #Model Reduction and Neural Networks #Fluid Dynamics and Turbulent Flows #Neural Networks and Applications

paper · doi:10.1016/j.cma.2024.116813

Citations

• Deterministic Nonperiodic Flow
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
• Toward Causal Representation Learning
• When and why PINNs fail to train: A neural tangent kernel perspective

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