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Machine learning approaches for elastic localization linkages in high-contrast composite materials

There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships...

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Detalles Bibliográficos
Autores principales: Liu, Ruoqian, Yabansu, Yuksel C., Agrawal, Ankit, Kalidindi, Surya R., Choudhary, Alok N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713466/
https://www.ncbi.nlm.nih.gov/pubmed/31523612
http://dx.doi.org/10.1186/s40192-015-0042-z
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author Liu, Ruoqian
Yabansu, Yuksel C.
Agrawal, Ankit
Kalidindi, Surya R.
Choudhary, Alok N.
author_facet Liu, Ruoqian
Yabansu, Yuksel C.
Agrawal, Ankit
Kalidindi, Surya R.
Choudhary, Alok N.
author_sort Liu, Ruoqian
collection PubMed
description There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.
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spelling pubmed-67134662019-09-13 Machine learning approaches for elastic localization linkages in high-contrast composite materials Liu, Ruoqian Yabansu, Yuksel C. Agrawal, Ankit Kalidindi, Surya R. Choudhary, Alok N. Integr Mater Manuf Innov Research There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique. Springer Berlin Heidelberg 2015-12-04 2015 /pmc/articles/PMC6713466/ /pubmed/31523612 http://dx.doi.org/10.1186/s40192-015-0042-z Text en © Liu et al. 2015 https://creativecommons.org/licenses/by/4.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Liu, Ruoqian
Yabansu, Yuksel C.
Agrawal, Ankit
Kalidindi, Surya R.
Choudhary, Alok N.
Machine learning approaches for elastic localization linkages in high-contrast composite materials
title Machine learning approaches for elastic localization linkages in high-contrast composite materials
title_full Machine learning approaches for elastic localization linkages in high-contrast composite materials
title_fullStr Machine learning approaches for elastic localization linkages in high-contrast composite materials
title_full_unstemmed Machine learning approaches for elastic localization linkages in high-contrast composite materials
title_short Machine learning approaches for elastic localization linkages in high-contrast composite materials
title_sort machine learning approaches for elastic localization linkages in high-contrast composite materials
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713466/
https://www.ncbi.nlm.nih.gov/pubmed/31523612
http://dx.doi.org/10.1186/s40192-015-0042-z
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