Item Infomation
Title: |
A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation |
Authors: |
Martin W., Hess Annalisa, Quaini Gianluigi, Rozza |
Issue Date: |
2023 |
Publisher: |
Springer |
Abstract: |
This work introduces a novel approach for data-driven model reduction of time-dependent parametric partial differential equations. Using a multi-step procedure consisting of proper orthogonal decomposition, dynamic mode decomposition, and manifold interpolation, the proposed approach allows to accurately recover field solutions from a few large-scale simulations. Numerical experiments for the Rayleigh-Bénard cavity problem show the effectiveness of such multi-step procedure in two parametric regimes, i.e., medium and high Grashof number. The latter regime is particularly challenging as it nears the onset of turbulent and chaotic behavior. |
Description: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s10444-023-10016-4 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7605 |
Appears in Collections |
OER - Khoa học Tự nhiên |
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