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physics.flu-dyn2025
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…
physics.flu-dyn2021★ 14 cited
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…
physics.flu-dyn2019★ 3 cited
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…