Weighted reduced order methods for uncertainty quantification in computational fluid dynamics

2023/03/25 by Julien Genovese, Genovese, Julien, Francesco Ballarin +5
Physics and Astronomy · Decision Sciences · Engineering · #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Fluid Dynamics and Turbulent Flows

paper · pdf · doi:10.48550/arxiv.2303.14432

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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