Generative Subgrid-Scale Modeling
Abstract
The mismatch between the a-priori and a-posteriori error is ubiquitous in data-driven subgrid-scale (SGS) modeling, which is an important ingredient in large eddy simulations. In this work, we investigate the cause of this mismatch in depth and attribute it to two issues: data imbalance and multi-valuedness. Based on this understanding, we propose a generative modeling approach for the SGS stresses that resolves the issue of multi-valuedness and demonstrate its effectiveness in the Kuramoto-Sivashinsky equation.
Cite
Text
Zhao et al. "Generative Subgrid-Scale Modeling." ICLR 2025 Workshops: MLMP, 2025.Markdown
[Zhao et al. "Generative Subgrid-Scale Modeling." ICLR 2025 Workshops: MLMP, 2025.](https://mlanthology.org/iclrw/2025/zhao2025iclrw-generative/)BibTeX
@inproceedings{zhao2025iclrw-generative,
title = {{Generative Subgrid-Scale Modeling}},
author = {Zhao, Jiaxi and Arisaka, Sohei and Li, Qianxiao},
booktitle = {ICLR 2025 Workshops: MLMP},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/zhao2025iclrw-generative/}
}