3 papers
cs.LG2026
Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs
Benjamin D. Shaffer, Shawn Koohy, Brooks Kinch +2
We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PDEs) which preserve structure and accur…
cs.LG2026
When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
Sifan Wang, Shawn Koohy, Yiping Lu +1
Physics-informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training often breaks down on challeng…
cs.LG2025
SGPT-PINNs: Sparse and Small models for PDEs
Yajie Ji, Yanlai Chen, Shawn Koohy
We propose SGPT-PINN, a sparse and small model for solving parametric partial differential equations (PDEs). Similar to Small Language Models (SLMs), SGPT-PINN is tailored…