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Entropy-driven Optimal Sub-sampling of Fluid Dynamics for Developing Machine-learned Surrogates...

by Wesley H Brewer, Muralikrishnan Gopalakrishnan Meena, Aditya Kashi, Katarzyna Borowiec, Siyan Liu
Publication Type
Conference Paper
Book Title
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
Publication Date
Page Numbers
73 to 80
Publisher Location
New York, United States of America
Conference Name
The 4th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Conference Location
Denver, Colorado, United States of America
Conference Sponsor
Conference Date

Optimal sub-sampling of large datasets from fluid dynamics simulations is essential for training reduced-order machine learned models. A method using Shannon entropy was developed to weight flow features according to their level of information content, such that the most informative features can be extracted and used for training a surrogate model. The method is demonstrated in the canonical flow over a cylinder problem simulated with OpenFOAM. Both time-independent predictions and temporal forecasting were investigated as well as two types of prediction targets: local per-grid-point predictions and global per-time-step predictions. When tested on training a surrogate model, results indicate that our entropy-based sampling method typically outperforms random sampling and yields more reproducible results in less iterations. Finally, the method was used to train a surrogate model for modeling turbulence in magnetohydrodynamic flows, which revealed various challenges and opportunities for future research.