most citedGPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

math.NA2023★ 1 cited

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…

math.NA2021

Fast optimal mass transport via reduced basis methods for the Monge-Ampre equation

Shijin Hou, Yanlai Chen, Yinhua Xia

Repeatedly solving the parameterized optimal mass transport (pOMT) problem is a frequent task in applications such as image registration and adaptive grid generation. It is thus cr…