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Communication Dans Un Congrès Année : 2012

A regression based non-intrusive method using separated representation for uncertainty quantification

Résumé

This paper aims at handling high dimensional uncertainty propagation problems by proposing a tensor product approximation method based on regression techniques. The underlying assumption is that the model output functional can be well represented in a separated form, as a sum of elementary tensors in the stochastic tensor product space. The proposed method consists in constructing a tensor basis with a greedy algorithm and then in computing an approximation in the generated approximation space using regression with sparse regularization. Using appropriate regularization techniques, the regression problems are well posed for only few sample evaluations and they provide accurate approximations of model outputs.
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Dates et versions

hal-01007795 , version 1 (17-03-2019)

Identifiants

Citer

Prashant Rai, Mathilde Chevreuil, Anthony Nouy, Jayant Sen Gupta. A regression based non-intrusive method using separated representation for uncertainty quantification. ASME 2012 11th Biennal Conference on Engineering Systems design and Analysis (ESDA 2012), 2012, Nantes, France. ⟨10.1115/ESDA2012-82301⟩. ⟨hal-01007795⟩
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