{"ID":2857987,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07904","arxiv_id":"2510.07904","title":"Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.07904","url_pdf":"https://arxiv.org/pdf/2510.07904v1","authors":"[\"Enrico Ampellio\",\"Blazhe Gjorgiev\",\"Giovanni Sansavini\"]","published":"2025-10-09T07:59:16Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}