67 citations · 87 across the 6 of their papers we have counts for
6 papers
Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
Shyam Sankaran, Hanwen Wang, Paris Perdikaris
Neural PDE solvers have followed the scaling trajectory of vision and language, with recent foundation models reaching billions of parameters. We argue that scale is a poor substit…
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
Sifan Wang, Shyam Sankaran, Xiantao Fan +2
Turbulent fluid flows are among the most computationally demanding problems in science, requiring enormous computational resources that become prohibitive at high flow speeds. Phys…
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
Sifan Wang, Tong-Rui Liu, Shyam Sankaran +1
Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods.…
CViT: Continuous Vision Transformer for Operator Learning
Sifan Wang, Jacob H Seidman, Shyam Sankaran +3
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various…
An Expert's Guide to Training Physics-informed Neural Networks
Sifan Wang, Shyam Sankaran, Hanwen Wang +1
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE…
Respecting causality is all you need for training physics-informed neural networks
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 exhi…