Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach
- 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.
- 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
- Journal Information:
- AIAA SCITECH 2022 Forum, Conference: AIAA SCITECH 2022 Forum, Held Virtually, San Diego, CA (United States), 3-7 Jan 2022
- Country of Publication:
- United States
- Language:
- English
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