Poster
in
Workshop: Machine Learning Multiscale Processes
Generative subgrid-scale modeling
Jiaxi Zhao · Sohei Arisaka · Qianxiao Li
Keywords: [ chaotic system ] [ Subgrid-scale model ] [ generative model ]
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.