Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
type: Item
instance of: scholarly article
title: Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
author: Saurabh Balkrishna Tandale
author: Franz Bamer
author: M. Stoffel
author: Bernd Markert
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2022-11-11T00:00:00Z
Mathematics Subject Classification ID: 74S05
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 74D10
zbMATH DE Number: 7615018
zbMATH Keywords: physics informed
zbMATH Keywords: deep learning
zbMATH Keywords: recurrent neural network
zbMATH Keywords: viscoplasticity
zbMATH Keywords: implicit recurrent neural network integration scheme
describes a project that uses: TensorFlow
describes a project that uses: DiffSharp
describes a project that uses: Keras
describes a project that uses: PINN
describes a project that uses: NSFnets
describes a project that uses: Adam
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: j.cma.2022.115668
OpenAlex ID: W4304776784
Wikidata QID: Q114952515
cites work: Q5270493
cites work: Artificial neural networks in structural dynamics: a new modular radial basis function approach vs. convolutional and feedforward topologies
cites work: Q5243583
cites work: Machine Learning for Fluid Mechanics
cites work: A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
cites work: Plasticity including the Bauschinger effect, studied by a neural network approach
cites work: On the parameter identification problem for failure criteria in anisotropic bodies
cites work: \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
cites work: A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model
cites work: A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
cites work: Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
cites work: A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
cites work: Smart constitutive laws: inelastic homogenization through machine learning
cites work: Data-driven computational mechanics
cites work: Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
cites work: Smart finite elements: a novel machine learning application
cites work: Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis
cites work: An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture
cites work: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
cites work: NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
cites work: A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
cites work: Mechanics of Solid Materials
cites work: Accuracy and stability of integration algorithms for elastoplastic constitutive relations
cites work: Q5480663
cites work: Learning representations by back-propagating errors
DOI: 10.1016/J.CMA.2022.115668
description: scientific article; zbMATH DE number 7615018