Differentiable Fluid Dynamics Package
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Updated
Jul 11, 2024 - Python
Differentiable Fluid Dynamics Package
High performance computational platform in Python for the spectral Galerkin method
Generative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields
🌊 Framework for studying fluid dynamics with numerical simulations using Python (publish-only mirror). The main repo is hosted on https://foss.heptapod.net (Gitlab fork supporting Mercurial).
A synthetic, isotropic turbulence generator for constant density flows that enforces the discrete divergence-free condition.
Multi-fidelity Generative Deep Learning Turbulent Flows
Python implementation of Typhoon algorithm: dense estimation of 2D-3D optical flow on wavelet bases.
ML-based turbulence modeling for astrophysics
Post processing routines for analysing PTV data.
Simulation tool that utilises a Fourier domain adaptive optics model to enable rapid Monte Carlo characterisation of free space optical links between the Earth and satellites
Generative turbulence model TurbDiff as proposed in "From Zero to Turbulence: Generative Modeling for 3D Flow Simulation", ICLR 2024
Model of propagating blobs in 1D and 2D
Python Package for Statistical Analysis of Turbulence Data
[Journal of Turbulence, DCC 2022] Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers
Ensemble Synthetic Eddy Method implemented in Python
Python and Paraview postprocessing tools for use with NREL's SOWFA
Applying a Multi-Layer Perceptron Deep Neural Network to predict Lift and Drag performance of airfoils
A high performance data-driven and differentiable SPH fluid solver
📡 🌀 A platform to use speckle patterns to describe atmospheric turbolence
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