\(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

DOI10.1016/j.cma.2020.112875zbMath1436.74012OpenAlexW3006475788MaRDI QIDQ2309352

Yousef Heider, Kun Wang, WaiChing Sun

Publication date: 31 March 2020

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.2020.112875

zbMATH Keywords

crystal plasticityrecurrent neural networkLie algebraanisotropic materialsspecial orthogonal groupmachine learning

Mathematics Subject Classification ID

Anisotropy in solid mechanics (74E10) Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05)

Related Items (26)

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling ⋮ Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks ⋮ A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning ⋮ Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling ⋮ A finite micro-rotation material point method for micropolar solid and fluid dynamics with three-dimensional evolving contacts and free surfaces ⋮ An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture ⋮ Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints ⋮ Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures ⋮ 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 ⋮ Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis ⋮ A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content ⋮ Distance-preserving manifold denoising for data-driven mechanics ⋮ Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter ⋮ Model-free data-driven identification algorithm enhanced by local manifold learning ⋮ Comparing field data using Alpert multi-wavelets ⋮ A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data ⋮ Deep autoencoders for physics-constrained data-driven nonlinear materials modeling ⋮ A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks ⋮ A unified water/ice kinematics approach for phase-field thermo-hydro-mechanical modeling of frost action in porous media ⋮ Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity ⋮ Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening ⋮ Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response ⋮ Material Modeling via Thermodynamics-Based Artificial Neural Networks ⋮ Residual stresses in gas tungsten arc welding: a novel phase-field thermo-elastoplasticity modeling and parameter treatment framework

Uses Software

• LSTM
• LIBSVM
• SciPy
• Scikit
• TensorFlow
• Keras

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Reservoir computing approaches to recurrent neural network training
• Discrete micromechanics of elastoplastic crystals in the finite deformation range
• Metrics for 3D rotations: comparison and analysis
• Computational inelasticity
• Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
• Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions.
• Computational thermomechanics of crystalline rock. I: A combined multi-phase-field/crystal plasticity approach for single crystal simulations
• An updated Lagrangian LBM-DEM-FEM coupling model for dual-permeability fissured porous media with embedded discontinuities
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning
• Model-free data-driven inelasticity
• A micromorphically regularized Cam-Clay model for capturing size-dependent anisotropy of geomaterials
• Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
• A graph theoretic framework for representation, exploration and analysis on computed states of physical systems
• Adversarial uncertainty quantification in physics-informed neural networks
• Computational thermo-hydro-mechanics for multiphase freezing and thawing porous media in the finite deformation range
• A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
• Derivation of heterogeneous material laws via data-driven principal component expansions
• A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
• Data-driven computational mechanics
• A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain
• Lie-group interpolation and variational recovery for internal variables
• A plasticity theory for porous solids
• Plasticity
• A Simple Derivation of Representations for Non-Polynomial Constitutive Equations in Some Cases of Anisotropy
• Crystal Plasticity
• Computational Statistics
• Modelling of shearing behaviour of a residual soil with Recurrent Neural Network
• The computation of the exponential and logarithmic mappings and their first and second linearizations
• Some implications of work hardening and ideal plasticity

This page was built for publication: \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials