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Multi-fidelity Bayesian Optimization in Engineering Design

Salvato in:
Pubblicato in:arXiv.org (Dec 9, 2024), p. n/a
Autore principale: Bach Do
Altri autori: Zhang, Ruda
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Accuracy
Design optimization
Algorithms
Gaussian process
Bayesian analysis
Multiple objective analysis
Design engineering
Uncertainty
Optimization techniques
Accesso online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
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045 0 |b d20241209 
100 1 |a Bach Do 
245 1 |a Multi-fidelity Bayesian Optimization in Engineering Design 
260 |b Cornell University Library, arXiv.org  |c Dec 9, 2024 
513 |a Working Paper 
520 3 |a Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization. 
653 |a Accuracy 
653 |a Design optimization 
653 |a Algorithms 
653 |a Gaussian process 
653 |a Bayesian analysis 
653 |a Multiple objective analysis 
653 |a Design engineering 
653 |a Uncertainty 
653 |a Optimization techniques 
700 1 |a Zhang, Ruda 
773 0 |t arXiv.org  |g (Dec 9, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2892799202/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2311.13050