Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
type: Item
instance of: scholarly article
title: Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
zbMATH Open document ID: 1440.74258
author: Xiang Li
author: Shaoqing Cui
author: Chengcheng Luo
author: Zhuo Zhuang
author: Zhan-Li Liu
author: Chenfeng Li
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2020-04-16T00:00:00Z
full work available at URL: cronfa48290
Mathematics Subject Classification ID: 74M25
Mathematics Subject Classification ID: 74E05
zbMATH DE Number: 7189763
zbMATH Keywords: heterogeneous materials
zbMATH Keywords: shale
zbMATH Keywords: deep learning
zbMATH Keywords: stochastic reconstruction
describes a project that uses: ImageNet
describes a project that uses: AlexNet
describes a project that uses: AdaBoost.MH
describes a project that uses: darch
MaRDI profile type: Publication
OpenAlex ID: W2908848055
cites work: The Elastic Moduli of Heterogeneous Materials
cites work: An algorithm for computing the effective linear elastic properties of heterogeneous materials: three-dimensional results for composites with equal phase Poisson ratios
cites work: A new approach to modelling the effective thermal conductivity of heterogeneous materials
cites work: Q4363509
cites work: Proper orthogonal decomposition and its applications-part. I: Theory
cites work: A decision-theoretic generalization of on-line learning and an application to boosting
cites work: Support-vector networks
cites work: A logical calculus of the ideas immanent in nervous activity
cites work: Learning representations by back-propagating errors
cites work: Reducing the Dimensionality of Data with Neural Networks
cites work: Q4216231
cites work: Q4253789
cites work: Numerical implementation of a neural network based material model in finite element analysis
cites work: Modelling and Simulation of Electro- and Magnetorheological Fluid Dampers
cites work: A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
cites work: Q4398829
cites work: Q4845459
Wikidata QID: Q128574223
DOI: 10.1016/J.CMA.2019.01.005
description: scientific article; zbMATH DE number 7189763