Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow

TL;DR

This paper introduces a shape-invariant 3D variational autoencoder designed for super-resolution in turbulence flow, leveraging deep learning to enhance the understanding and modeling of complex fluid dynamic phenomena.

Contribution

It presents a novel deep generative model that combines shape invariance with super-resolution capabilities specifically for turbulence data.

Findings

Improved super-resolution reconstruction of turbulence flow fields.

Enhanced integration of multiscale turbulence models with deep learning.

Demonstrated effectiveness of the proposed autoencoder in turbulence analysis.

Abstract

Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in particular, benefits from the growing availability of high-dimensional data obtained through experiments, field observations, and large-scale simulations spanning multiple spatio-temporal scales. This report presents a concise overview of both classical and deep learningbased approaches to turbulence modeling. It further investigates two specific challenges at the intersection of fluid dynamics and machine learning: the integration of multiscale turbulence models with deep learning architectures, and the application of deep generative models for super-resolution reconstruction