97 citations · 112 across the 5 of their papers we have counts for
6 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…
Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN
Shahed Rezaei, Ahmad Moeineddin, Ali Harandi
We applied physics-informed neural networks to solve the constitutive relations for nonlinear, path-dependent material behavior. As a result, the trained network not only satisfies…
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains
Ali Harandi, Ahmad Moeineddin, Michael Kaliske +2
Physics-informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary con…
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Shahed Rezaei, Ali Harandi, Ahmad Moeineddin +2
Physics-informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem. We employ several ideas from the finite element method (FEM) to enh…