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Found 3 papers, 2 papers with code
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows.
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques.
When is settling important for particle concentrations in wall-bounded turbulent flows?
While it may be thought that settling can be ignored when the settling parameter $Sv\equiv v_s/u_\tau$ is small ($v_s$ - Stokes settling velocity, $u_\tau$ - fluid friction velocity), we show that even in this regime the settling may make a leading order contribution to the concentration profiles.
Fluid Dynamics Atmospheric and Oceanic Physics Geophysics
