Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response

DOI10.1016/j.cma.2022.115668OpenAlexW4304776784WikidataQ114952515 ScholiaQ114952515MaRDI QIDQ2096901

Marcus Stoffel, Saurabh Balkrishna Tandale, Franz Bamer, Bernd Markert

Publication date: 11 November 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2022.115668

zbMATH Keywords

recurrent neural networkviscoplasticitydeep learningimplicit recurrent neural network integration schemephysics informed

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Finite element methods applied to problems in solid mechanics (74S05) Nonlinear constitutive equations for materials with memory (74D10)

Related Items (6)

Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder-decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates ⋮ A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content ⋮ Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics ⋮ Adaptive task decomposition physics-informed neural networks ⋮ Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response ⋮ Transfer learning of recurrent neural network‐based plasticity models

Uses Software

• TensorFlow
• Keras
• DiffSharp
• Adam
• GitHub
• PINN
• NSFnets

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the parameter identification problem for failure criteria in anisotropic bodies
• Plasticity including the Bauschinger effect, studied by a neural network approach
• Smart finite elements: a novel machine learning application
• Smart constitutive laws: inelastic homogenization through machine learning
• A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
• Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
• Artificial neural networks in structural dynamics: a new modular radial basis function approach vs. convolutional and feedforward topologies
• An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture
• A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
• A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
• \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
• A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
• A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
• Data-driven computational mechanics
• Machine Learning for Fluid Mechanics
• Accuracy and stability of integration algorithms for elastoplastic constitutive relations
• Mechanics of Solid Materials
• Learning representations by back-propagating errors
• Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis

This page was built for publication: Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response