To reduce the computational cost, surrogate models have been widely used to replace expensive simulations in design under uncertainty. However, most existing methods may introduce significant errors when the training data is limited. This paper presents a confidence-driven design optimization (CDDO) framework to manage surrogate model uncertainty for probabilistic design optimization. In this study, a confidence-based Gaussian process (GP) modeling technique is developed to handle the surrogate model uncertainty in system performance predictions by taking both the prediction mean and variance into account. With a target confidence level, the confidence-based GP models are used to reduce the probability of underestimating the probability of failure in reliability assessment. In addition, a new sensitivity analysis method is proposed to approximate the sensitivity of the reliability at the target confidence level with respect to design variables, and thus facilitate the CDDO framework. Three case studies are introduced to demonstrate the effectiveness of the proposed approach.
Confidence-Driven Design Optimization Using Gaussian Process Metamodeling With Insufficient Data
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 1, 2018; final manuscript received July 20, 2018; published online September 18, 2018. Assoc. Editor: Mian Li.
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Li, M., and Wang, Z. (September 18, 2018). "Confidence-Driven Design Optimization Using Gaussian Process Metamodeling With Insufficient Data." ASME. J. Mech. Des. December 2018; 140(12): 121405. https://doi.org/10.1115/1.4040985
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