Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
This is an incremental overview for researchers in fluid dynamics and machine learning, focusing on existing challenges without presenting new solutions.
The paper tackles the integration of multiscale turbulence models with deep learning and the use of deep generative models for super-resolution reconstruction in turbulence flow, but does not report specific results or numbers.
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