A proportional expected improvement criterion-based multi-fidelity sequential optimization method

Huang, Hanyan; Liu, Zecong; Zheng, Hongyu; Xu, Xiaoyu; Duan, Yanhui

doi:10.1007/s00158-022-03484-7

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Different strategies have been proposed in the literature [35,36,37,38,39,40]. A possible approach is to solve Eq.…”

Section: Multi-fidelity Bayesian Optimization (Mfbo)mentioning

confidence: 99%

Multi-Fidelity Sequential Bayesian Optimization and Reliability Assessment Method for the Design of Complex Systems

Espoeys,

Brevault,

Balesdent

et al. 2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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Solving a Reliability-Based Design Optimization (RBDO) problem is a challenging task, especially in the presence of complex systems that are modeled using computationally expensive numerical solvers. This topic has been extensively investigated in the literature. Amongst the proposed strategies, the Sequential Optimization and Reliability Assessment (SORA) method implements a decoupled resolution of the RBDO problem by sequentially iterating between deterministic optimization and reliability analysis. The idea of SORA is to solve, at each iteration, an equivalent deterministic optimization of the RBDO problem. This is achieved in a region that is considered as feasible, by shifting the constraints by a distance derived from the target failure probability. Once the deterministic optimization is performed, a reliability analysis phase is achieved by solving another optimization problem, in order to update the shifting vector for the next deterministic optimization problem. In this paper, we propose to solve the optimization problems in SORA with a Bayesian approach through an active learning strategy based on multi-fidelity surrogate models. First, the surrogate models are built in an augmented space where design variables and uncertain variables are combined. Second, the computational cost of SORA is reduced by taking advantage of lower fidelity solvers to build the surrogates. The merits of this innovative framework are demonstrated on an analytical test case as a well as on a realistic test case involving the optimization of a sounding rocket subject to a probability constraint on the target altitude to reach.

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“…Different strategies have been proposed in the literature [35,36,37,38,39,40]. A possible approach is to solve Eq.…”

Section: Multi-fidelity Bayesian Optimization (Mfbo)mentioning

confidence: 99%

Multi-Fidelity Sequential Bayesian Optimization and Reliability Assessment Method for the Design of Complex Systems

Espoeys,

Brevault,

Balesdent

et al. 2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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Sequential sampling for functional estimation via Sieve

Benevento,

Ahadi,

Gupta

et al. 2024

Quality & Reliability Eng

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Sequential sampling methods are often used to estimate functions describing models subjected to time‐intensive simulations or expensive experiments. These methods provide guidelines for point selection in the domain to capture maximum information about the function. However, in most sequential sampling methods, determining a new point is a time‐consuming process. In this paper, we propose a new method, named Sieve, to sequentially select points of an initially unknown function based on the definition of proper intervals. In contrast with existing methods, Sieve does not involve function estimation at each iteration. Therefore, it presents a greater computational efficiency for achieving a given accuracy in estimation. Sieve brings in tools from computational geometry to subdivide regions of the domain efficiently. Further, we validate our proposed method through numerical simulations and two case studies on the calibration of internal combustion engines and the optimal exploration of an unknown environment by a mobile robot.

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Multifidelity co-kriging metamodeling based on multivariate data fusion for dynamic fit improvement of injection mechanism in squeeze casting

You,

Lin,

et al. 2025

Alexandria Engineering Journal

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A proportional expected improvement criterion-based multi-fidelity sequential optimization method

Cited by 5 publications

References 33 publications

Multi-Fidelity Sequential Bayesian Optimization and Reliability Assessment Method for the Design of Complex Systems

Multi-Fidelity Sequential Bayesian Optimization and Reliability Assessment Method for the Design of Complex Systems

Sequential sampling for functional estimation via Sieve

Multifidelity co-kriging metamodeling based on multivariate data fusion for dynamic fit improvement of injection mechanism in squeeze casting

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