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Adaptive active subspace-based efficient multifidelity materials design
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DOI：10.1016/j.matdes.2021.110001.png)
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摘要
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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/).
Keyword:
Adaptive dimensionality reduction
Active subspace
Multifidelity design
Bayesian optimization
PSP relationships
Dual-phase materials
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7.9
论文数:
1.9W
被引数:
9.8W
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