most citedFinite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs

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

collaborators

2 papers

cs.CE2024★ 2 cited

Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs

Shahed Rezaei, Reza Najian Asl, Kianoosh Taghikhani +3

We introduce a method that combines neural operators, physics-informed machine learning, and standard numerical methods for solving PDEs. The proposed approach extends each of the…

cs.LG2024

Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations

Shahed Rezaei, Ahmad Moeineddin, Michael Kaliske +1

We present a method that employs physics-informed deep learning techniques for parametrically solving partial differential equations. The focus is on the steady-state heat equation…