34 citations · 52 across the 5 of their papers we have counts for
5 papers
Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling
Tomek Jaroslawski, Aakash Patil, Beverley McKeon
The high dimensionality and complex dynamics of turbulent flows in urban street canyons present significant challenges for wind and environmental engineering, particularly in addre…
Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows
Aakash Patil, Jonathan Viquerat, Elie Hachem
A convolutional encoder-decoder-based transformer model is proposed for autoregressively training on spatio-temporal data of turbulent flows. The prediction of future fluid flow fi…
Robust deep learning for emulating turbulent viscosities
Aakash Patil, Jonathan Viquerat, George El Haber +1
From the simplest models to complex deep neural networks, modeling turbulence with machine learning techniques still offers multiple challenges. In this context, the present contri…
An open source MATLAB package to perform basic and advanced statistical analysis of turbulence data and other complex systems
André Fuchs, Swapnil Kharche, Matthias Wächter +1
We present a user-friendly open-source MATLAB\textsuperscript{\textregistered} package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl v…
Development of Deep Learning Methods for Inflow Turbulence Generation
Aakash Vijay Patil
The present work proposes an inflow turbulence generation strategy using deep learning methods. This is achieved with the help of an autoencoder architecture with two different typ…