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An active learning Kriging-assisted method for reliability-based design optimization under distributional probability-box model
Structural and Multidisciplinary Optimization ( IF 3.9 ) Pub Date : 2020-07-10 , DOI: 10.1007/s00158-020-02604-5
Jinhao Zhang , Liang Gao , Mi Xiao , Soobum Lee , Amin Toghi Eshghi

Due to lack of sufficient data and information in engineering practice, it is often difficult to obtain precise probability distributions of some uncertain variables and parameters in reliability-based design optimization (RBDO). In this paper, distributional probability-box (p-box) model is employed to quantify these uncertain variables and parameters. To reduce the computational cost in RBDO associated with expensive and time-consuming constraints, an active learning Kriging-assisted method is proposed. In this method, the sequential optimization and reliability assessment (SORA) method is extended for RBDO under distributional p-box model. Kriging metamodels are constructed to make the replacement of actual constraints. To remove unnecessary computational expense on constructing Kriging metamodels, a screening criterion is built and employed for the judgment of active constraints in RBDO. Then, an active learning function is defined to find out update samples, which are adopted for sequentially refining Kriging metamodel of each active constraint by focusing on its limit-state surface (LSS) around the most probable target point (MPTP) at the solution of SORA. Several examples, including a welded beam problem and a piezoelectric energy harvester design, are provided to test the accuracy and efficiency of the proposed active learning Kriging-assisted method.



中文翻译:

分布概率盒模型下基于可靠性的主动学习Kriging优化设计

由于工程实践中缺乏足够的数据和信息,在基于可靠性的设计优化(RBDO)中通常难以获得某些不确定变量和参数的精确概率分布。本文采用分布概率盒(p-box)模型对这些不确定变量和参数进行量化。为了降低RBDO中昂贵且费时的约束条件下的计算成本,提出了一种主动学习克里格辅助方法。该方法扩展了分布式p-box模型下RBDO的顺序优化和可靠性评估(SORA)方法。构造克里金元模型以替换实际约束。为了消除在构造Kriging元模型时不必要的计算费用,建立筛选标准并将其用于RBDO中的活动约束判断。然后,定义一个主动学习函数以找出更新样本,然后采用该样本通过关注最可能目标点(MPTP)周围的极限状态表面(LSS)来依次细化每个主动约束的Kriging元模型。 SORA。提供了几个示例,包括焊接梁问题和压电能量收集器设计,以测试所提出的主动学习Kriging辅助方法的准确性和效率。通过将其集中在SORA解决方案中最有可能的目标点(MPTP)周围的极限状态表面(LSS)来依次细化每个活动约束的Kriging元模型。提供了几个示例,包括焊接梁问题和压电能量收集器设计,以测试所提出的主动学习Kriging辅助方法的准确性和效率。通过将其集中在SORA解决方案中最有可能的目标点(MPTP)周围的极限状态表面(LSS)来依次细化每个活动约束的Kriging元模型。提供了几个示例,包括焊接梁问题和压电能量收集器设计,以测试所提出的主动学习Kriging辅助方法的准确性和效率。

更新日期:2020-07-10
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