2 citations · 2 across the 2 of their papers we have counts for
3 papers
Data-Free Weak-Form Staggered Neural Operators for Magneto-Mechanical Coupling in Finite-Strain Elastomers
Alireza Yazdandousthamedani, Ahmad Moeineddin, Reza Najian Asl +2
Magneto-active elastomers, as a class of smart materials, exhibit strongly coupled magnetic and mechanical behavior at finite strains. Considering variations in microstructure, mat…
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…
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…