A deep material network approach for predicting the thermomechanical response of composites-Reference-Cited by-同舟云学术

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A deep material network approach for predicting the thermomechanical response of composites

Published:2024-03 Issue: Volume:272 Page:111177
ISSN:1359-8368
Container-title:Composites Part B: Engineering
language:en
Short-container-title:Composites Part B: Engineering

Author:

Shin Dongil^ORCID,Alberdi Ryan,Lebensohn Ricardo A.,Dingreville Rémi^ORCID

Funder

NNSA DNN

Sandia National Laboratories

U.S. Department of Energy

Los Alamos National Laboratory

Center for Integrated Nanotechnologies

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,Ceramics and Composites

Reference44 articles.

1. Additive manufacturing of polymeric composites from material processing to structural design;Yuan;Composites B,2021

2. Lightweight composite structures in transport: design, manufacturing, analysis and performance;Njuguna,2016

3. Deep learning framework for material design space exploration using active transfer learning and data augmentation;Kim;NPJ Comput Mater,2021

4. Digital-twin-enhanced quality prediction for the composite materials;Wang;Engineering,2023

5. FE2 multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials;Feyel;Comput Methods Appl Mech Engrg,2000

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep material network for thermal conductivity problems: Application to woven composites;Computer Methods in Applied Mechanics and Engineering;2024-11

2. Rethinking materials simulations: Blending direct numerical simulations with neural operators;npj Computational Materials;2024-07-04

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