1 citations · 1 across the 5 of their papers we have counts for
5 papers
A Physics-Conditioned Neural Operator for Generalization of Atrioventricular Valve Mechanics across Pressure and Tissue Properties
Shawn Koohy, Wensi Wu, Matthew A Jolley +1
Mitral and tricuspid regurgitation are the most common regurgitant valvular lesions, yet only a minority of severe cases undergo corrective surgery. Rapid assessment of valve mecha…
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…
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…
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…
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs
Yanlai Chen, Shawn Koohy
Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the express…