Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
This addresses scalability issues in engineering design optimization under uncertainty, though it appears incremental as an enhancement to existing surrogate-assisted methods.
The paper tackles the challenge of optimizing large, complex engineering designs under uncertainty, where existing methods lack scalability and precision. The proposed multi-level Kriging-based approach achieves orders of magnitude improvements in both speed and accuracy compared to state-of-the-art methods.
Engineering design involves demanding models encompassing many decision variables and uncontrollable parameters. In addition, unavoidable aleatoric and epistemic uncertainties can be very impactful and add further complexity. The state-of-the-art adopts two steps, uncertainty quantification and design optimization, to optimize systems under uncertainty by means of robust or stochastic metrics. However, conventional scenario-based, surrogate-assisted, and mathematical programming methods are not sufficiently scalable to be affordable and precise in large and complex cases. Here, a multi-level approach is proposed to accurately optimize resource-intensive, high-dimensional, and complex engineering problems under uncertainty with minimal resources. A non-intrusive, fast-scaling, Kriging-based surrogate is developed to map the combined design/parameter domain efficiently. Multiple surrogates are adaptively updated by hierarchical and orthogonal decomposition to leverage the fewer and most uncertainty-informed data. The proposed method is statistically compared to the state-of-the-art via an analytical testbed and is shown to be concurrently faster and more accurate by orders of magnitude.