Generative subgrid-scale modeling
Track: New scientific result
Keywords: Subgrid-scale model, generative model, chaotic system
TL;DR: We explain the mismatch between a-priori and a-posteriori error in subgrid-scale modeling and propose a generative model to resolve it.
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.
Presenter: ~Jiaxi_Zhao1
Submission Number: 15
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