sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Xilinx, Inc., San Jose, CA (United States)
Bayesian optimization (BO) is an effective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various levels of computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. Finally, the sMF-BO-2CoGP method is demonstrated using a series of analytical examples and a flip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC04-94AL85000; 17020246; NA0003525
- OSTI ID:
- 1605731
- Report Number(s):
- SAND-2020-3026J; 684693
- Journal Information:
- Journal of Computing and Information Science in Engineering, Vol. 20, Issue 3; ISSN 1530-9827
- Publisher:
- ASMECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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