Adaptive active subspace-based efficient multifidelity materials design
| Autor: | Ankit Srivastava, Jaylen James, Douglas Allaire, Abhilash Molkeri, Richard Couperthwaite, Raymundo Arroyave, Danial Khatamsaz |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Mechanical Engineering Bayesian optimization Dual-phase materials Material Design Materials design Active subspace computer.software_genre Multifidelity design Adaptive dimensionality reduction Mechanics of Materials TA401-492 General Materials Science Data mining PSP relationships Engineering design process computer Design space Materials of engineering and construction. Mechanics of materials Subspace topology Curse of dimensionality |
| Zdroj: | Materials & Design, Vol 209, Iss, Pp 110001-(2021) |
| ISSN: | 0264-1275 |
| Popis: | 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 dimensionality 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 adaptive active subspace method within the multi-fidelity BO framework is presented. |
| Databáze: | OpenAIRE |
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