期刊
MATERIALS & DESIGN
卷 209, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2021.110001
关键词
Adaptive dimensionality reduction; Active subspace; Multifidelity design; Bayesian optimization; PSP relationships; Dual-phase materials
资金
- U.S. National Science Foundation [NSF-CMMI-1663130]
- [NSF-DGE-1545403]
- [NSF-IIS-1849085]
- [NSF-IIS-1835690]
This study introduces an adaptive active subspace method to efficiently handle high-dimensional design space problems in material design. This method can accelerate the design process by prioritizing searches in important regions of the high-dimensional design space.
Materials design calls for an optimal exploration and exploitation of the process-structure-property (PSP) relationships to produce materials with targeted properties. Recently, we developed and deployed a closed-loop multi-information source fusion (multi-fidelity) Bayesian Optimization (BO) framework to optimize the mechanical performance of a dual-phase material by adjusting the material composition and processing parameters. While promising, BO frameworks tend to underperform as the dimensional-ity of the problem increases. Herein, we employ an adaptive active subspace method to efficiently handle the large dimensionality of the design space of a typical PSP-based material design problem within our multi-fidelity BO framework. Our adaptive active subspace method significantly accelerates the design process by prioritizing searches in the important regions of the high-dimensional design space. A detailed discussion of the various components and demonstration of three approaches to implementing the adap-tive active subspace method within the multi-fidelity BO framework is presented. (c) 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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