Functional order-reduced Gaussian Processes based machine-learning emulators for probabilistic constitutive modelling-Reference-Cited by-同舟云学术

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Functional order-reduced Gaussian Processes based machine-learning emulators for probabilistic constitutive modelling

Published:2023-10 Issue: Volume:173 Page:107695
ISSN:1359-835X
Container-title:Composites Part A: Applied Science and Manufacturing
language:en
Short-container-title:Composites Part A: Applied Science and Manufacturing

Author:

Ding Chensen^ORCID,Chen Yang,Rappel Hussein,Dodwell Tim

Funder

Fundamental Research Funds for the Central Universities

Engineering and Physical Sciences Research Council

Peking University

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Mechanics of Materials,Ceramics and Composites

Reference46 articles.

1. Metamodeling of constitutive model using Gaussian process machine learning;Wang;J Mech Phys Solids,2021

2. Bayesian inference to identify parameters in viscoelasticity;Rappel;Mech Time Depend Mater,2018

3. A constitutive model for soft materials incorporating viscoelasticity and Mullins effect;Lu;J Appl Mech,2017

4. Combination and application of machine learning and computational mechanics;Li;Chin Sci Bull,2019

5. A review of artificial neural networks in the constitutive modeling of composite materials;Liu;Composites B,2021

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