Learning constitutive relations using symmetric positive definite neural networks
From MaRDI portal
Publication:2128348
DOI10.1016/j.jcp.2020.110072OpenAlexW3015176898MaRDI QIDQ2128348
Kailai Xu, Eric Darve, Daniel Z. Huang
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.00265
Related Items (25)
Image inversion and uncertainty quantification for constitutive laws of pattern formation ⋮ Efficient derivative-free Bayesian inference for large-scale inverse problems ⋮ On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling ⋮ Compliance minimisation of smoothly varying multiscale structures using asymptotic analysis and machine learning ⋮ The parametrized superelement approach for lattice joint modelling and simulation ⋮ Machine learning constitutive models of elastomeric foams ⋮ Iterated Kalman methodology for inverse problems ⋮ Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity ⋮ A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling ⋮ Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials ⋮ Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks ⋮ Bayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary framework ⋮ Distance-preserving manifold denoising for data-driven mechanics ⋮ Incremental neural controlled differential equations for modeling of path-dependent material behavior ⋮ DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress ⋮ Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions ⋮ An equivariant neural operator for developing nonlocal tensorial constitutive models ⋮ A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity ⋮ Modular machine learning-based elastoplasticity: generalization in the context of limited data ⋮ Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method ⋮ An FE-DMN method for the multiscale analysis of short fiber reinforced plastic components ⋮ Learning viscoelasticity models from indirect data using deep neural networks ⋮ Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data ⋮ Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Identification of elastic parameters by displacement field measurement
- Machine learning strategies for systems with invariance properties
- Multiscale methods for composites: A review
- Finite element analysis of V-ribbed belts using neural network based hyperelastic material model
- Determination of the size of the representative volume element for random composites: Statistical and numerical approach.
- \(FE^2\) multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- Second-order work criterion: from material point to boundary value problems
- Computational homogenization of nonlinear elastic materials using neural networks
- A Time Integration Algorithm for Structural Dynamics With Improved Numerical Dissipation: The Generalized-α Method
- Analysis of Composite Materials—A Survey
- Toward realization of computational homogenization in practice
- Line search algorithms with guaranteed sufficient decrease
- A Limited Memory Algorithm for Bound Constrained Optimization
- Solving high-dimensional partial differential equations using deep learning
- Effect of Depth and Width on Local Minima in Deep Learning
- Genetic evolution of nonlinear material constitutive models
This page was built for publication: Learning constitutive relations using symmetric positive definite neural networks