2026-10-14T11:34:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33968549 2026-09-22T18:09:16Z category_146 category_15 category_16 category_45 category_734 category_931 category_811 portal_87 item_type_3 month_year_09_2026

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes Yu Tan (616353) Yongxiang Li (116212) Xiaowu Dai (10073580) Kwok-Leung Tsui (808417) Space Science Neuroscience Physiology Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Black-box systems Surrogate modeling Functional-output FANOVA Expected conditional variance Fuselage assembly <p>Functional ANOVA (FANOVA) is a widely used variance-based sensitivity analysis tool. However, studies on functional-output FANOVA remain relatively scarce, especially for black-box computer experiments, which often involve complex and nonlinear functional-output relationships with unknown data distributions. Conventional approaches often rely on predefined basis functions or parametric structures that lack the flexibility to capture complex nonlinear relationships. Additionally, strong assumptions about the underlying data distributions further limit their ability to achieve data-driven orthogonal effect decomposition. To address these challenges, this study proposes a functional-output orthogonal additive Gaussian process (FOAGP) to efficiently perform data-driven orthogonal effect decomposition. By enforcing a conditional orthogonality constraint on the separable prior process, the proposed functional-output orthogonal additive kernel enables data-driven orthogonality without requiring prior distributional assumptions. The FOAGP framework also provides analytical formulations for local Sobol’ indices and expected conditional variance sensitivity indices, enabling comprehensive sensitivity analysis by capturing both global and local effect significance. Validation through simulation studies and a real case study on fuselage shape control confirms the model’s effectiveness in orthogonal effect decomposition and variance decomposition, demonstrating its practical value in engineering applications.</p> 2026-09-22T18:09:16Z Dataset Dataset 10.6084/m9.figshare.33968549.v1 https://figshare.com/articles/dataset/Effect_Decomposition_of_Functional-Output_Computer_Experiments_via_Orthogonal_Additive_Gaussian_Processes/33968549 CC BY 4.0