142 citations · 378 across the 19 of their papers we have counts for
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A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures
Ali Harandi, Hooman Danesh, Kevin Linka +2
A novel physics-informed operator learning technique based on spectral methods is introduced to model the complex behavior of heterogeneous materials. The Lippmann-Schwinger operat…
A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
Rasoul Najafi Koopas, Shahed Rezaei, Natalie Rauter +2
A spatiotemporal deep learning framework is proposed that is capable of 2D full-field prediction of fracture in concrete mesostructures. This framework not only predicts fractures…
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…
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space
Rasoul Najafi Koopas, Shahed Rezaei, Natalie Rauter +2
In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space informa…
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…
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…