Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)

• URL: http://arxiv.org/abs/2502.04685v1
• Date: Fri, 07 Feb 2025 06:17:31 GMT
• Title: Capturing Extreme Events in Turbulence using an Extreme Variational Autoencoder (xVAE)
• Authors: Likun Zhang, Kiran Bhaganagar, Christopher K. Wikle,
• Abstract summary: Extreme variational Autoencoder (xVAE) embeds a max-infinitely divisible process with heavy-tailed distributions into a standard VAE framework.<n>xVAE reduces system dimensionality by learning non-linear latent representations of data.
• Score: 2.393499494583
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Turbulent flow fields are characterized by extreme events that are statistically intermittent and carry a significant amount of energy and physical importance. To emulate these flows, we introduce the extreme variational Autoencoder (xVAE), which embeds a max-infinitely divisible process with heavy-tailed distributions into a standard VAE framework, enabling accurate modeling of extreme events. xVAEs are neural network models that reduce system dimensionality by learning non-linear latent representations of data. We demonstrate the effectiveness of xVAE in large-eddy simulation data of wildland fire plumes, where intense heat release and complex plume-atmosphere interactions generate extreme turbulence. Comparisons with the commonly used Proper Orthogonal Decomposition (POD) modes show that xVAE is more robust in capturing extreme values and provides a powerful uncertainty quantification framework using variational Bayes. Additionally, xVAE enables analysis of the so-called copulas of fields to assess risks associated with rare events while rigorously accounting for uncertainty, such as simultaneous exceedances of high thresholds across multiple locations. The proposed approach provides a new direction for studying realistic turbulent flows, such as high-speed aerodynamics, space propulsion, and atmospheric and oceanic systems that are characterized by extreme events.

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