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 formationEfficient derivative-free Bayesian inference for large-scale inverse problemsOn physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling samplingCompliance minimisation of smoothly varying multiscale structures using asymptotic analysis and machine learningThe parametrized superelement approach for lattice joint modelling and simulationMachine learning constitutive models of elastomeric foamsIterated Kalman methodology for inverse problemsLearning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental DataDeep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticityA mechanics‐informed artificial neural network approach in data‐driven constitutive modelingThermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materialsPhysically enhanced training for modeling rate-independent plasticity with feedforward neural networksBayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary frameworkDistance-preserving manifold denoising for data-driven mechanicsIncremental neural controlled differential equations for modeling of path-dependent material behaviorDPK: Deep Neural Network Approximation of the First Piola-Kirchhoff StressImproved Analysis of PINNs: Alleviate the CoD for Compositional SolutionsAn equivariant neural operator for developing nonlocal tensorial constitutive modelsA mechanics-informed deep learning framework for data-driven nonlinear viscoelasticityModular machine learning-based elastoplasticity: generalization in the context of limited dataLearning constitutive models from microstructural simulations via a non-intrusive reduced basis methodAn FE-DMN method for the multiscale analysis of short fiber reinforced plastic componentsLearning viscoelasticity models from indirect data using deep neural networksPredicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network dataFrame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids


Uses Software


Cites Work


This page was built for publication: Learning constitutive relations using symmetric positive definite neural networks