Sequential optimization and moment-based method for efficient probabilistic design

Zhen-hu, Wang; Li, Hao; Chen, Ziming; Li, Luoxing; He, Hong

doi:10.1007/s00158-020-02494-7

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…X = 𝜎 𝐷 (𝑊 𝐷 𝐸 + 𝑏 𝐷 ) (10) where 𝐸 denotes the encoding features, that is, the output of the hidden layer. X stands for the reconstruction data, that is, the output of the output layer.…”

Section: Deep Sparse Autoencodermentioning

confidence: 99%

“…This approach is well regarded due to its computational efficiency, but it does not scale in practice because it leans on the minimum performance target point (MPTP). Some other approaches pertaining to this group have appeared in 10,11 and so on. The last group contains the single-loop approach, which usually leverages the Karush-Kuhn-Tucker optimality condition to establish an equivalent DDO and solves it by canonical optimization strategies.…”

Section: Introductionmentioning

confidence: 99%

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A global strategy based on deep learning for time‐dependent optimal reliability design

Ling

Kuo

2023

Quality & Reliability Eng

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Time‐dependent reliability‐based design optimization (RBDO) is a computationally tough problem that needs to be addressed urgently. The difficulty of solving the time‐dependent RBDO mainly comes from the time‐dependent reliability analysis involved in probabilistic constraints, which itself is one of the thorny problems in the reliability community and makes the computational cost become much more onerous. In this paper, a deep‐learning‐assisted approach is proposed to solve the time‐dependent RBDO. The proposed approach leverages the classification capability of the deep learning, and constructs the alternative model for the actual probabilistic constraint function in the so‐called augmented reliability space, so as to make the trained alternative model accurate wherever it will be invoked. Moreover, a sequential sampling technique utilizing the classification probability provided by the deep learning is proposed to further reduce the computational cost. Then, the time‐dependent reliability analysis involved in the time‐dependent RBDO is conducted by the cheaper alternative model instead of the original computing‐intensive probabilistic constraint function, which evidently reduces the computational burden. The presented examples showcase the performance of the proposed approach. Especially, for the complicated engineering application, the proposed approach saves about 10% of the computational cost compared with the existing methods.

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Section: Deep Sparse Autoencodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A global strategy based on deep learning for time‐dependent optimal reliability design

Ling

Kuo

2023

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Decoupled methods aim at separating reliability analysis from the optimization loop. The sequential optimization and reliability assessment (SORA) method is one of the most popular decoupled methods, in which RBDO is decoupled into a sequence of deterministic optimization and reliability assessment [15], [16]. The above works addressed RBDO involving only aleatory uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization

et al. 2023

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Hybrid reliability-based design optimization (HRBDO) can provide an effective way to obtain the optimum design in the presence of both random and interval variables. HRBDO is typically described as a nested optimization model. It is computationally expensive when directly solving the HRBDO problem by the nested optimization method. To address this issue, this paper develops an efficient decoupled HRBDO method that aims at performance measure function approximation by the adaptive Kriging model (KPMFA). The proposed KPMFA method includes three main blocks, namely hybrid inverse reliability analysis, performance measure function approximation, and equivalent deterministic optimization. In KPMFA, the adaptation of the adaptive chaos control (ACC) algorithm for inverse reliability analysis that accommodates interval variables is developed. Moreover, an adaptive strategy with two-stage of enrichment for the Kriging model is developed to approximate performance measure functions on the region of interest. Then, the optimization can be proceeded using the Kriging model of performance measure functions. Finally, five illustrative HRBDO problems are investigated to demonstrate the accuracy and efficiency of the proposed KPMFA method.INDEX TERMS Hybrid reliability-based design optimization (HRBDO); random and interval variables; adaptive kriging model; performance measure function approximation

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“…The latter are then shifted at the next cycle to make the minimum more reliable. Different methods (Torii et al (2016); Jiang et al (2020); Wang et al (2020); Zhang et al (2021)) use the same approach as SORA but propose a different shifting strategy. Benchmarks of the classical reliability approaches have been carried out in Aoues and Chateauneuf (2010) and Lopez and Beck (2012).…”

Section: Introductionmentioning

confidence: 99%

A two-step procedure for time-dependent reliability-based design optimization involving piece-wise stationary Gaussian processes

Cousin

Garnier

Guiton

et al. 2022

Struct Multidisc Optim

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We consider in this paper a time-dependent reliability-based design optimization (RBDO) problem with constraints involving the maximum and/or the integral of a random process over a time interval. We focus especially on problems where the process is a stationary or a piece-wise stationary Gaussian process. A two-step procedure is proposed to solve the problem. First, we use ergodic theory and extreme value theory to reformulate the original constraints into timeindependent ones. We obtain an equivalent RBDO problem for which classical algorithms perform poorly. The second step of the procedure is to solve the reformulated problem with a new method introduced in this paper and based on an adaptive kriging strategy well suited to the reformulated constraints called AK-ECO for Adaptive Kriging for Expectation Constraints Optimization. The procedure is applied to two toy examples involving a harmonic oscillator subjected to random forces. It is then applied to an optimal design problem for a floating offshore wind turbine.

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Sequential optimization and moment-based method for efficient probabilistic design

Cited by 7 publications

References 36 publications

A global strategy based on deep learning for time‐dependent optimal reliability design

A global strategy based on deep learning for time‐dependent optimal reliability design

Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization

A two-step procedure for time-dependent reliability-based design optimization involving piece-wise stationary Gaussian processes

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