Weighted reduced order methods for uncertainty quantification in computational fluid dynamics

Julien Genovese; Francesco Ballarin; Gianluigi Rozza; Claudio Canuto

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arXiv · 2303.14432

Weighted reduced order methods for uncertainty quantification in computational fluid dynamics

Julien Genovese·Francesco Ballarin·Gianluigi Rozza·Claudio Canuto

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Abstract

In this manuscript we propose and analyze weighted reduced order methods for stochastic Stokes and Navier-Stokes problems depending on random input data (such as forcing terms, physical or geometrical coefficients, boundary conditions). We will compare weighted methods such as weighted greedy and weighted POD with non-weighted ones in case of stochastic parameters. In addition we will analyze different sampling and weighting choices to overcome the curse of dimensionality with high dimensional parameter spaces.

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Julien Genovese, Francesco Ballarin, Gianluigi Rozza, Claudio Canuto. 2023-03-25. Weighted reduced order methods for uncertainty quantification in computational fluid dynamics. https://arxiv.org/abs/2303.14432

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