Bridging Operator Learning and Conditioned Neural Fields: A Unifying Perspective

1 code implementation • 22 May 2024 • Sifan Wang, Jacob H Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris

Our contributions can be viewed as a first step towards adapting advanced computer vision architectures for building more flexible and accurate machine learning models in physical sciences.

Operator learning

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.

Attribute