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Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation

机译：使用自适应多保真仿真的计算昂贵模型的鲁棒优化

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摘要

Computationally expensive models are increasingly employed in the design process of engineering products and systems. Robust design in particular aims to obtain designs that exhibit near-optimal performance and low variability under uncertainty. Surrogate models are often employed to imitate the behaviour of expensive computational models. Surrogates are trained from a reduced number of samples of the expensive model. A crucial component of the performance of a surrogate is the quality of the training set. Problems occur when sampling fails to obtain points located in an area of interest and/or where the computational budget only allows for a very limited number of runs of the expensive model. This paper employs a Gaussian process emulation approach to perform efficient single-loop robust optimisation of expensive models. The emulator is enhanced to propagate input uncertainty to the emulator output, allowing single-loop robust optimisation. Further, the emulator is trained with multi-fidelity data obtained via adaptive sampling to maximise the quality of the training set for the given computational budget. An illustrative example is presented to highlight how the method works, before it is applied to two industrial case studies.
机译：在工程产品和系统的设计过程中越来越多地采用计算昂贵的模型。特别是旨在获得在不确定性下表现出近乎最佳性能和低变异性的设计。替代模型通常用于模仿昂贵的计算模型的行为。替代工人从昂贵的昂贵模型的缩小量数训练。代理表现的关键组成部分是培训集的质量。当采样未能获得位于感兴趣区域的点和/或计算预算仅允许非常有限的昂贵模型运行时，发生问题。本文采用高斯流程仿真方法来执行昂贵模型的有效单环路鲁棒优化。仿真器增强以将输入不确定性传播到仿真器输出，允许单环路鲁棒优化。此外，仿真器具有通过自适应采样获得的多保真数据培训，以最大化给定的计算预算的训练的质量。提出了一个说明性示例以强调该方法如何工作，然后在应用于两个工业案例研究之前。

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