activity
20192026
most citedA finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains

38 citations · 42 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LG2026

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

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★ 38 cited

A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains

Yusuke Yamazaki, Ali Harandi, Mayu Muramatsu +5

We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (…

cs.LG2024

A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations

Shahed Rezaei, Reza Najian Asl, Shirko Faroughi +6

To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of the finite element method with physics-informe…