An Expert's Guide to Training Physics-informed Neural Networks

1 code implementation • 16 Aug 2023 • Sifan Wang, Shyam Sankaran, Hanwen Wang, Paris Perdikaris

Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints.

Respecting causality is all you need for training physics-informed neural networks

3 code implementations • 14 Mar 2022 • Sifan Wang, Shyam Sankaran, Paris Perdikaris

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior.

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