142 citations · 378 across the 19 of their papers we have counts for
21 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…
Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage
Rishabh Arora, Lisa Scheunemann, Tim Brepols +1
Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated based on domain knowledge and…
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…
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…
Comparative analysis of phase-field and intrinsic cohesive zone models for fracture simulations in multiphase materials with interfaces: Investigation of the influence of the microstructure on the fracture properties
Rasoul Najafi Koopas, Shahed Rezaei, Natalie Rauter +2
This study evaluates four widely used fracture simulation methods, comparing their computational expenses and implementation complexities within the Finite Element (FE) framework w…
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…
AI enhanced finite element multiscale modelling and structural uncertainty analysis of a functionally graded porous beam
Da Chen, Nima Emami, Shahed Rezaei +5
The local geometrical randomness of metal foams brings complexities to the performance prediction of porous structures. Although the relative density is commonly deemed as the key…
A thermo-mechanical phase-field fracture model: application to hot cracking simulations in additive manufacturing
Hui Ruan, Shahed Rezaei, Yangyiwei Yang +2
Thermal fracture is prevalent in many engineering problems and is one of the most devastating defects in metal additive manufacturing. Due to the interactive underlying physics inv…
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…
An anisotropic cohesive fracture model: advantages and limitations of length-scale insensitive phase-field damage models
Shahed Rezaei, Ali Harandi, Tim Brepols +1
The goal of the current work is to explore direction-dependent damage initiation and propagation within an arbitrary anisotropic solid. In particular, we aim at developing anisotro…
Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence
Jaber Rezaei Mianroodi, Shahed Rezaei, Nima H. Siboni +2
The elastic properties of materials derive from their electronic and atomic nature. However, simulating bulk materials fully at these scales is not feasible, so that typically homo…
Mixed timestepping schemes for nonsmooth mechanics with high frequency damping
Shahed Rezaei, Thorsten Schindler
This work deals with the integration of nonsmooth flexible multibody systems with impacts and dry friction. We develop a framework which improves a non-impulsive trajectory of stat…