<scp>Data</scp>‐physics driven reduced order homogenization

DOI10.1002/nme.7178OpenAlexW4309862306MaRDI QIDQ6071405

Yang Yu, Jacob Fish

Publication date: 23 November 2023

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nme.7178

zbMATH Keywords

neural networkextrapolationinterpretabilityBayesian inferenceopen hole quasi-isotropic platepseudo-nonlocal finite element method

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Finite element methods applied to problems in solid mechanics (74S05) Homogenization, determination of effective properties in solid mechanics (74Q99)

Cites Work

• Unnamed Item
• Multiple scale eigendeformation-based reduced order homogenization
• Wavelet based reduced order models for microstructural analyses
• An introduction to computational micromechanics.
• Eigendeformation-based reduced order homogenization for failure analysis of heterogeneous materials
• Two scale analysis of heterogeneous elastic-plastic materials with asymptotic homogenization and Voronoi cell finite element model
• Computational plasticity for composite structures based on mathematical homogenization: Theory and practice
• Nonuniform transformation field analysis
• Simulation of the multi-scale convergence in computational homogenization approaches
• \(FE^2\) multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials
• A numerical method for computing the overall response of nonlinear composites with complex microstructure
• Elastic-plastic analysis of arbitrary heterogeneous materials with the Voronoi cell finite element method
• A note on rapid genetic calibration of artificial neural networks
• Bayesian inference of non-linear multiscale model parameters accelerated by a deep 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
• A nonlinear manifold-based reduced order model for multiscale analysis of heterogeneous hyperelastic materials
• Computational analysis of nonlinear composite structures using the nonuniform transformation field analysis
• Computational homogenization of nonlinear elastic materials using neural networks
• Hierarchical model reduction at multiple scales
• Toward realization of computational homogenization in practice
• Transformation field analysis of inelastic composite materials
• A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

This page was built for publication: <scp>Data</scp>‐physics driven reduced order homogenization