Capturing Extreme Events in Turbulence Using an Extreme Variational Autoencoder
Abstract
Turbulent flows are characterized by intense generation of turbulent kinetic energy through nonlinear physical processes which cascade from the large- to small-scale structures in a forward energy cascade, which is chaotic in nature, and statistically intermittent. Using a recently developed extreme variational autoencoder (XVAE), the turbulent flow fields are replicated to a high order of accuracy. In this extended abstract, we demonstrate XVAE as a powerful alternative to the classical Proper Orthogonal Decomposition (POD) technique for reconstructing large-eddy-simulation (LES) data for scalar temperatures from a buoyant turbulent field at a high Reynolds number of $10^{10}$.
Cite
Text
Zhang et al. "Capturing Extreme Events in Turbulence Using an Extreme Variational Autoencoder." NeurIPS 2024 Workshops: BDU, 2024.Markdown
[Zhang et al. "Capturing Extreme Events in Turbulence Using an Extreme Variational Autoencoder." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/zhang2024neuripsw-capturing/)BibTeX
@inproceedings{zhang2024neuripsw-capturing,
title = {{Capturing Extreme Events in Turbulence Using an Extreme Variational Autoencoder}},
author = {Zhang, Likun and Wikle, Christopher and Bhaganagar, Kiran},
booktitle = {NeurIPS 2024 Workshops: BDU},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/zhang2024neuripsw-capturing/}
}