Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910033970724864 |
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| author | Ampellio, Enrico Gjorgiev, Blazhe Sansavini, Giovanni |
| author_facet | Ampellio, Enrico Gjorgiev, Blazhe Sansavini, Giovanni |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_07904 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty Ampellio, Enrico Gjorgiev, Blazhe Sansavini, Giovanni Systems and Control Machine Learning 60G15 (Primary) 68T37, 90C26 (Secondary) C.4; G.1.6; G.3; G.4; I.2.3; I.5.1; J.2 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. |
| title | Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty |
| topic | Systems and Control Machine Learning 60G15 (Primary) 68T37, 90C26 (Secondary) C.4; G.1.6; G.3; G.4; I.2.3; I.5.1; J.2 |
| url | https://arxiv.org/abs/2510.07904 |