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PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit by Sancarlos, Abel; Champaney, Victor; Duval, Jean-Louis; Cueto, Elias; Chinesta, Francisco is a scholarly article available to read on EtoBox.

What is PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit about?

Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics whose corresponding multi-parametric solutions can be viewed as a sort of computational vademecum that, once computed offline, can be then used in a variety of real-time engineering applications including optimization, inverse analysis, uncertainty propagation or simulation based control. Sometimes, these multi-parametric problems can be solved by using advanced model order reduction -- MOR -- techniques. However, when the solution of these multi-parametric problems becomes cumbersome, one possibility consists in solving the problem for a sample of the parametric values, and then creating a regression from all the computed solutions, to finally infer the solution for any choice of the problem parameters. However, addressing high-dimensionality at the low data limit, ensuring accuracy and avoiding overfitting constitutes a difficult challenge. The present paper aims at proposing and discussing different PGD-based advanced regressions enabling the just referred features.

Author

Sancarlos, Abel; Champaney, Victor; Duval, Jean-Louis; Cueto, Elias; Chinesta, Francisco

Published

2021

Language

EN