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Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization

Kovachki, Nikola and Liu, Burigede and Sun, Xingsheng and Zhou, Hao and Bhattacharya, Kaushik and Ortiz, Michael and Stuart, Andrew (2022) Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization. Mechanics of Materials, 165 . Art. No. 104156. ISSN 0167-6636. doi:10.1016/j.mechmat.2021.104156. https://resolver.caltech.edu/CaltechAUTHORS:20220121-968309000

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Abstract

The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material system whose properties are optimized by manipulating its underlying microstructure through processing. The specific configuration of the structure is then designed by characterizing the material in detail, and using this characterization along with physical principles in system level simulations and optimization. These have been advanced by multiscale modeling of materials, high-throughput experimentations, materials data-bases, topology optimization and other ideas. Still, developing materials for extreme applications involving large deformation, high strain rates and high temperatures remains a challenge. This article reviews a number of recent methods that advance the goal of designing materials targeted by specific applications.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.mechmat.2021.104156DOIArticle
https://arxiv.org/abs/2104.05918arXivDiscussion Paper
ORCID:
AuthorORCID
Kovachki, Nikola0000-0002-3650-2972
Liu, Burigede0000-0002-6518-3368
Sun, Xingsheng0000-0003-1527-789X
Zhou, Hao0000-0002-6011-6422
Bhattacharya, Kaushik0000-0003-2908-5469
Ortiz, Michael0000-0001-5877-4824
Stuart, Andrew0000-0001-9091-7266
Additional Information:© 2021 Elsevier Ltd. Received 12 April 2021, Revised 10 November 2021, Accepted 11 November 2021, Available online 27 November 2021. The work described in Sections 4 Machine-learning material behavior, 6 Uncertainty quantification across scales, 7 Concurrent optimization of material and structure was sponsored by the Army Research Laboratory, United States and was accomplished under Cooperative Agreement Number W911NF-12-2-0022. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein The work described in Section 3 was sponsored by the Air Force Office of Scientific Research, United States under the MURI Award FA9550-16-1-0566. The work described in Section 5 was sponsored by the Air Force Office of Scientific Research, United States through the Center of Excellence on High-Rate Deformation Physics of Heterogeneous Materials, Award FA9550-12-1-0091 and the Deutsche Forschungsgemeinschaft, Germany through the Sonderforschungsbereich 1060 ‘The mathematics of emergent effects’. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Army Research LaboratoryW911NF-12-2-0022
Air Force Office of Scientific Research (AFOSR)FA9550-16-1-0566
Air Force Office of Scientific Research (AFOSR)FA9550-12-1-0091
Deutsche Forschungsgemeinschaft (DFG)SFB 1060
Subject Keywords:Multiscale modeling; Materials by design; Machine learning; Uncertainty quantification
DOI:10.1016/j.mechmat.2021.104156
Record Number:CaltechAUTHORS:20220121-968309000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220121-968309000
Official Citation:Nikola Kovachki, Burigede Liu, Xingsheng Sun, Hao Zhou, Kaushik Bhattacharya, Michael Ortiz, Andrew Stuart, Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization, Mechanics of Materials, Volume 165, 2022, 104156, ISSN 0167-6636, https://doi.org/10.1016/j.mechmat.2021.104156.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113068
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Jan 2022 00:17
Last Modified:22 Jan 2022 00:17

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