Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate

DOI10.1016/j.cma.2023.115934OpenAlexW4320519002MaRDI QIDQ2693414

M. A. Maia, Pierre Kerfriden, I. B. C. M. Rocha, Frans P. van der Meer

Publication date: 20 March 2023

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

Full work available at URL: https://arxiv.org/abs/2209.07320

zbMATH Keywords

heterogeneous materialsmultiscalepath-dependencyartificial neural networks (ANNs)

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Biology and other natural sciences (92-XX)

Related Items

Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation

Uses Software

• Jive
• Adam
• F3DAM

Cites Work

• Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
• A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics
• Mesolevel modeling of failure in composite laminates: constitutive, kinematic and algorithmic aspects
• Artificial neural networks in numerical modelling of composites
• Micromechanical validation of a mesomodel for plasticity in composites
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Smart constitutive laws: inelastic homogenization through machine learning
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
• Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN)
• Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
• A machine learning based plasticity model using proper orthogonal decomposition
• Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks
• Model-data-driven constitutive responses: application to a multiscale computational framework
• A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method
• Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity
• Reduced order modeling strategies for computational multiscale fracture
• Dimensional hyper-reduction of nonlinear finite element models via empirical cubature
• POD-DEIM model order reduction for strain-softening viscoplasticity
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Computational homogenization for multiscale crack modeling. Implementational and computational aspects
• The determination of the elastic field of an ellipsoidal inclusion, and related problems
• Material Modeling via Thermodynamics-Based Artificial Neural Networks