Accelerating Flow Simulations using Online Dynamic Mode Decomposition
Abstract
We develop an on-the-fly reduced-order model (ROM) integrated with a flow simulation, gradually replacing a corresponding full-order model (FOM) of a physics solver. Unlike offline methods requiring a separate FOM-only simulation prior to model reduction, our approach constructs a ROM dynamically during the simulation, replacing the FOM when deemed credible. Dynamic mode decomposition (DMD) is employed for online ROM construction, with a single snapshot vector used for rank-1 updates in each iteration. Demonstrated on a flow over a cylinder with Re = 100, our hybrid FOM/ROM simulation is verified in terms of the Strouhal number, resulting in a 4.4 times speedup compared to the FOM solver.
Cite
@article{arxiv.2311.18715,
title = {Accelerating Flow Simulations using Online Dynamic Mode Decomposition},
author = {Seung Won Suh and Seung Whan Chung and Peer-Timo Bremer and Youngsoo Choi},
journal= {arXiv preprint arXiv:2311.18715},
year = {2023}
}
Comments
Presented at Machine Learning and the Physical Sciences Workshop, NeurIPS 2023