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Title: sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications

Journal Article · · Journal of Computing and Information Science in Engineering
DOI:https://doi.org/10.1115/1.4046697· OSTI ID:1605731
 [1];  [1];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. 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
Citation Metrics:
Cited by: 16 works
Citation information provided by
Web of Science

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