Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Khatamsaz, Danial; Arroyave, Raymundo; Allaire, Douglas L.

doi:10.2514/6.2022-0075

Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Conference · 29 December 2022 · AIAA SCITECH 2022 Forum

DOI:https://doi.org/10.2514/6.2022-0075· OSTI ID:1894254

Khatamsaz, Danial ^[1]; Arroyave, Raymundo ^[2]; Allaire, Douglas L. ^[2]

Texas A & M Univ., College Station, TX (United States); Texas A&M Engineering Experiment Station
Texas A & M Univ., College Station, TX (United States)

Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.

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Research Organization:: Texas A & M Univ., College Station, TX (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

DOE Contract Number:: AR0001427

OSTI ID:: 1894254

Conference Information:: Journal Name: AIAA SCITECH 2022 Forum

Country of Publication:: United States

Language:: English

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