5 papers · 1 filter
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…
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…
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 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 (…
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…