Surrogate modelling for UQ in Engineering
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2024-07-31
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Presentation
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Abstract
Uncertainty analyses typically require numerous function evaluations, which can become prohibitively costly for complex engineering models. To reduce computational effort, surrogate models can be used, which approximate the original model and are cheap to evaluate. We consider black-box methods, i.e., methods which do not need knowledge of the inner workings of the original model. In our lectures, we introduce the two popular surrogate modeling techniques polynomial chaos expansions and Kriging, and discuss their use in sensitivity and reliability analysis. We also explore extensions, such as the case of non-scalar output. The theory is illustrated with case studies and accompanied by practical exercises.
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unpublished
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9th GAMM Juniors Summer School: Uncertainty Quantification, Stochastic Partial Differential Equations and Risk Analysis (SAMM 2024)
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03962 - Sudret, Bruno / Sudret, Bruno
Notes
Invited speaker. Presentation held on July 31, 2024.