Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition

Conference · Wed Feb 19 04:00:00 EST 2025

OSTI ID:2584656

Zhang, Fengxue ^[1]; Desautels, Thomas A. ^[2]; Chen, Yuxin ^[3]

---

1. University of Chicago, Illinois, U.S.
2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
3. University of Chicacgo, Illinois, U.S.

Multi-fidelity Bayesian optimization (MFBO) is a powerful approach that utilizes lowfidelity, cost-effective sources to expedite the exploration and exploitation of a high-fidelity objective function. Existing MFBO methods with theoretical foundations either lack justification for performance improvements over single-fidelity optimization or rely on strong assumptions about the relationships between fidelity sources to construct surrogate models and direct queries to low-fidelity sources. To mitigate the dependency on cross-fidelity assumptions while maintaining the advantages of low-fidelity queries, we introduce a random sampling and partition-based MFBO framework with deep kernel learning. This framework is robust to cross-fidelity model misspecification and explicitly illustrates the benefits of low-fidelity queries. Our results demonstrate that the proposed algorithm effectively manages complex cross-fidelity relationships and efficiently optimizes the target fidelity function.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC52-07NA27344
• OSTI ID: 2584656
• Report Number(s): LLNL-CONF-872542; IM #: 1110934
• Country of Publication: United States
• Language: English