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.
Genovese, J., Ballarin, F., Rozza, G., Canuto, C., Weighted Reduced Order Methods for Uncertainty Quantification in Computational Fluid Dynamics, Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators: RAMSES, Springer Nature Switzerland AG, Cham 2024 <<LECTURE NOTES IN COMPUTATIONAL SCIENCE AND ENGINEERING>>,: 127-151. 10.1007/978-3-031-55060-7_6 [https://hdl.handle.net/10807/281977]
Weighted Reduced Order Methods for Uncertainty Quantification in Computational Fluid Dynamics
Ballarin, Francesco;
2024
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
