2 citations · 3 across the 3 of their papers we have counts for
5 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…
Tackling multiphysics problems via finite element-guided physics-informed operator learning
Yusuke Yamazaki, Reza Najian Asl, Markus Apel +2
This work presents a finite element-guided physics-informed operator learning framework for multiphysics problems with coupled partial differential equations (PDEs) on arbitrary do…
Neural-Initialized Newton: Accelerating Nonlinear Finite Elements via Operator Learning
Kianoosh Taghikhani, Yusuke Yamazaki, Jerry Paul Varghese +3
We propose a Newton-based scheme, initialized by neural operator predictions, to accelerate the parametric solution of nonlinear problems in computational solid mechanics. First, a…
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometries
Reza Najian Asl, Yusuke Yamazaki, Kianoosh Taghikhani +3
In this work, we introduce implicit Finite Operator Learning (iFOL) for the continuous and parametric solution of partial differential equations (PDEs) on arbitrary geometries. We…
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…