# Awesome Fluid Mechanics [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) ![Links CI](https://img.shields.io/github/workflow/status/lento234/awesome-fluid-dynamics/CI?label=Links&style=flat-square&labelColor=black) ![License](https://img.shields.io/github/license/lento234/awesome-fluid-dynamics?style=flat-square&color=blue&labelColor=000000) ![Last commit](https://img.shields.io/github/last-commit/lento234/awesome-fluid-dynamics?style=flat-square&labelColor=000000) ![Awesome](logo/awesome-logo.svg) A curated list of repositories related to fluid dynamics. *`Please send pull requests or raise issues to improve this list.`* ## Contents - [Educational](#educational) - [Notebooks](#notebooks) - [Lecture Series](#lecture-series) - [Books](#books) - [Meshing](#meshing) - [Computational Fluid Dynamics](#computational-fluid-dynamics) - [Finite Difference Methods (FDM)](#finite-difference-methods-fdm) - [Finite Element Methods (FEM)](#finite-element-methods-fem) - [Finite Volume Methods (FVM)](#finite-volume-methods-fvm) - [Spectral Methods](#spectral-methods) - [Vortex Methods / Panel Methods / Blade Element Methods](#vortex-methods--panel-methods--blade-element-methods) - [Immersed Boundary Methods (IBM)](#immersed-boundary-methods-ibm) - [Building Energy and Urban Environments](#building-energy-and-urban-environments) - [Shallow Water / Ocean Dynamics](#shallow-water--ocean-dynamics) - [Lattice Boltzmann Methods (LBM)](#lattice-boltzmann-methods-lbm) - [Design and Optimization](#design-and-optimization) - [Coupling](#coupling) - [Chemical Kinetics](#chemical-kinetics) - [Supersonic / Hypersonic Flow](#supersonic--hypersonic-flow) - [Differential programming](#differential-programming) - [Neural Networks for PDE](#neural-networks-for-pde) - [Graphics](#graphics) - [Other Techniques](#other-techniques) - [Experimental Fluid Dynamics](#experimental-fluid-dynamics) - [PIV / PTV](#piv--ptv) - [ML / Optical Flow](#ml--optical-flow) - [Post-processing and Data Analysis](#post-processing-and-data-analysis) - [Visualization](#visualization) - [2D Visualization](#2d-visualization) - [3D Visualization](#3d-visualization) - [Benchmarks and Datasets](#benchmarks-and-datasets) - [Datasets](#datasets) - [Benchmarks](#benchmarks) - [Reproducibility](#reproducibility) - [Community](#community) - [Related Topics](#related-topics) ## Educational ### Notebooks - [barbagroup/CFDPython](https://github.com/barbagroup/CFDPython) - A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes". ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/barbagroup/CFDPython/search?l=jupyter-notebook) - [gpeyre/numerical-tours](https://github.com/gpeyre/numerical-tours) - Numerical Tours of Signal Processing and other materials. ![MATLAB](logo/MATLAB.svg) ![Python](logo/Python.svg) ![Jupyter](logo/Jupyter.svg) ![julia](logo/julia.svg) ![R](logo/R.svg) - [jfavre/Visualization-training](https://github.com/jfavre/Visualization-training) - The material used for the Scientific Visualization course, organized online by the Swiss National Supercomputing Centre (CSCS) on May 17-18, 2021. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/jfavre/Visualization-training/search?l=jupyter-notebook) ### Lecture Series - [Steve Brunton/Fluid Dynamics](https://www.youtube.com/playlist?list=PLMrJAkhIeNNQWO3ESiccZmPssvUDFHL4M) - Prof. Brunton's lecture series on Fluid dynamics. ![YouTube](logo/YouTube.svg) - [Barry Belmont/NSF Fluid Mechanics Series](https://www.youtube.com/playlist?list=PL0EC6527BE871ABA3) - A collection of NFS Fluid Mechanics lecutre series from mid 20th century. ![YouTube](logo/YouTube.svg) ### Books - Brunton, S., & Kutz, J. (2019). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge: Cambridge University Press. [DOI :memo:](https://doi.org/10.1017/9781108380690) [Book :book:](http://databookuw.com/) - Castro, I. P., & Vanderwel, C. (2021). Turbulent Flows: An Introduction. IOP Publishing. [DOI :memo:](https://doi.org/10.1088/978-0-7503-3619-2) [Book :book:](https://github.com/cvanderwel/TurbulentFlows) - Thuerey, N., Holl, P., Mueller, M., Schnell, P., Trost, F., & Um, K. (2021). Physics-based Deep Learning. [Book :book:](https://physicsbaseddeeplearning.org/) ## Meshing - [nschloe/optimesh](https://github.com/nschloe/optimesh) - Mesh optimization, mesh smoothing. ![Python](logo/Python.svg) - [nschloe/pygmsh](https://github.com/nschloe/pygmsh) - Gmsh for Python. ![Python](logo/Python.svg) - [nschloe/meshio](https://github.com/nschloe/meshio) - I/O for exhaustive number of mesh file types. ![Python](logo/Python.svg) - [PyMesh/PyMesh](https://github.com/PyMesh/PyMesh) - Geometry Processing Library for Python. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [cnr-isti-vclab/meshlab](https://github.com/cnr-isti-vclab/meshlab) - The open source mesh processing system. ![C++](logo/cpp.svg) - [inducer/meshpy](https://github.com/inducer/meshpy) - 2D/3D simplicial mesh generator interface for Python (Triangle, TetGen, gmsh). ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [gmsh](https://en.wikipedia.org/wiki/Gmsh) - A three-dimensional finite element mesh generator with built-in pre- and post-processing facilities ![C++](logo/cpp.svg). ![Python](logo/Python.svg) ![julia](logo/julia.svg) - [CGAL/cgal](https://github.com/CGAL/cgal) - The Computational Geometry Algorithms Library (CGAL) is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. ![C++](logo/cpp.svg) ## Computational Fluid Dynamics ### Finite Difference Methods (FDM) - [p-costa/CaNS](https://github.com/p-costa/CaNS) - A code for fast, massively-parallel direct numerical simulations (DNS) of canonical flows. ![FORTRAN](logo/FORTRAN.svg) ### Finite Element Methods (FEM) - [JuliaFEM/JuliaFEM.jl](https://github.com/JuliaFEM/JuliaFEM.jl) - The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. ![julia](logo/julia.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/JuliaFEM/JuliaFEM.jl/search?l=jupyter-notebook) - [FEniCS/dolfinx](https://github.com/FEniCS/dolfinx) - Next generation FEniCS problem solving environment. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [deal.II](https://dealii.org/) - An open source finite element library. ![C++](logo/cpp.svg) - [firedrakeproject/firedrake](https://github.com/firedrakeproject/firedrake) - Firedrake is an automated system for the portable solution of partial differential equations using the finite element method (FEM). ![Python](logo/Python.svg) - [KratosMultiphysics/Kratos](https://github.com/KratosMultiphysics/Kratos) - Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [FluidityProject/fluidity](https://github.com/FluidityProject/fluidity) - An open-source computational fluid dynamics code with adaptive unstructured mesh capabilities. ![FORTRAN](logo/FORTRAN.svg) - [kinnala/scikit-fem](https://github.com/kinnala/scikit-fem) - Simple finite element assemblers. ![Python](logo/Python.svg) ### Finite Volume Methods (FVM) - [OpenFOAM/OpenFOAM-dev](https://github.com/OpenFOAM/OpenFOAM-dev) - OpenFOAM is a free, open source computational fluid dynamics (CFD) software package released by the OpenFOAM Foundation. ![C++](logo/cpp.svg) - [su2code/SU2](https://github.com/su2code/SU2) - SU2: An Open-Source Suite for Multiphysics Simulation and Design. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [cselab/aphros](https://github.com/cselab/aphros) - Finite volume solver for incompressible multiphase flows with surface tension. ![C++](logo/cpp.svg) - [code-saturne/code_saturne](https://github.com/code-saturne/code_saturne) - code_saturne public mirror. ![C++](logo/cpp.svg) ![FORTRAN](logo/FORTRAN.svg) ![Python](logo/Python.svg) - [DelNov/T-Flows](https://github.com/DelNov/T-Flows) - T-Flows (stands for Turbulent Flows) is a Computational Fluid Dynamics (CFD) program, originally developed at Delft University of Technology, the Netherlands. ![FORTRAN](logo/FORTRAN.svg) - [ucns3d-team/UCNS3D](https://github.com/ucns3d-team/UCNS3D) - Unstructured Compressible Navier Stokes 3D code (UCNS3D). ![FORTRAN](logo/FORTRAN.svg) - [weymouth/WaterLily.jl](https://github.com/weymouth/WaterLily.jl) - Fast and simple fluid simulator in Julia. ![julia](logo/julia.svg) - [MFlowCode/MFC](https://github.com/mflowcode/MFC) - MFC is a compressible multiphase fluid solver with high-order-accurate shock- and interface-capturing and GPU support via OpenACC. ![FORTRAN](logo/FORTRAN.svg) ### Spectral Methods - [DedalusProject/dedalus](https://github.com/DedalusProject/dedalus) - A flexible framework for solving PDEs with modern spectral methods. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/DedalusProject/dedalus/search?l=jupyter-notebook) - [FourierFlows/FourierFlows.jl](https://github.com/FourierFlows/FourierFlows.jl) - Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains. ![julia](logo/julia.svg) - [Nek5000/Nek5000](https://github.com/Nek5000/Nek5000) - NEK5000 is an spectral element CFD code developed at the Mathematics and Computer Science Division of Argonne National Laboratory. ![FORTRAN](logo/FORTRAN.svg) - [spectralDNS/shenfun](https://github.com/spectralDNS/shenfun) - High performance computational platform in Python for the spectral Galerkin method. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/spectralDNS/shenfun/search?l=jupyter-notebook) ### Vortex Methods / Panel Methods / Blade Element Methods - [vortexmethods/VM2D](https://github.com/vortexmethods/VM2D) - VM2D: Open-Source Code for 2D Flow Simulation by Using Meshless Lagrangian Vortex Methods. ![C++](logo/cpp.svg) - [markstock/vic2d](https://github.com/markstock/vic2d) - Two-dimensional semi-Lagrangian vortex method for very low viscosity fluid simulation. ![C++](logo/cpp.svg) ![FORTRAN](logo/FORTRAN.svg) - [gdeskos/DVMpp](https://github.com/gdeskos/DVMpp) - 2D Discrete Vortex Method Code written in C++. ![C++](logo/cpp.svg) - [byuflowlab/FLOWUnsteady](https://github.com/byuflowlab/FLOWUnsteady) - Mixed-fidelity unsteady aerodynamics and aeroacoustics. ![julia](logo/julia.svg) - [Applied-Scientific-Research/Omega2D](https://github.com/Applied-Scientific-Research/Omega2D) - Two-dimensional flow solver with GUI using vortex particle and boundary element methods. ![C++](logo/cpp.svg) - [camUrban/PteraSoftware](https://github.com/camUrban/PteraSoftware) - A fast, easy-to-use, and open-source software package for analyzing flapping-wing flight. ![Python](logo/Python.svg) ### Immersed Boundary Methods (IBM) - [cwrowley/ibpm](https://github.com/cwrowley/ibpm) - Immersed Boundary Projection Method (IBPM). ![C++](logo/cpp.svg) - [barbagroup/PetIBM](https://github.com/barbagroup/PetIBM) - PetIBM - toolbox and applications of the immersed-boundary method on distributed-memory architectures. ![C++](logo/cpp.svg) - [barbagroup/cuIBM](https://github.com/barbagroup/cuIBM) - cuIBM: a GPU-based immersed boundary method code. ![C++](logo/cpp.svg) - [ChenguangZhang/sdfibm](https://github.com/ChenguangZhang/sdfibm) - Immersed boundary method empowered by signed distance field, and OpenFOAM. ![C++](logo/cpp.svg) ### Building Energy and Urban Environments - [NREL/EnergyPlus](https://github.com/NREL/EnergyPlus) - EnergyPlus™ is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption and water use in buildings. ![C++](logo/cpp.svg) - [uDALES/u-dales](https://github.com/uDALES/u-dales) - uDALES: large-eddy-simulation software for urban flow, dispersion and microclimate modelling. ![FORTRAN](logo/FORTRAN.svg) - [ladybug-tools/butterfly](https://github.com/ladybug-tools/butterfly) - :butterfly: A light python API for creating and running OpenFoam cases for CFD simulation. ![Python](logo/Python.svg) ### Shallow Water / Ocean Dynamics - [jostbr/shallow-water](https://github.com/jostbr/shallow-water) - Python model solving the shallow water equations (linear momentum, nonlinear continuity). ![Python](logo/Python.svg) - [team-ocean/veros](https://github.com/team-ocean/veros) - The versatile ocean simulator, in pure Python, powered by JAX. ![Python](logo/Python.svg) - [CliMA/Oceananigans.jl](https://github.com/CliMA/Oceananigans.jl) - Julia software for fast, friendly, flexible, data-driven, ocean-flavored fluid dynamics on CPUs and GPUs. ![julia](logo/julia.svg) - [OceanParcels/parcels](https://github.com/OceanParcels/parcels) - Parcels (Probably A Really Computationally Efficient Lagrangian Simulator) is a set of Python classes and methods to create customisable particle tracking simulations using output from Ocean Circulation models. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/OceanParcels/parcels/search?l=jupyter-notebook) ### Lattice Boltzmann Methods (LBM) - [aromanro/LatticeBoltzmann](https://github.com/aromanro/LatticeBoltzmann) - A 2D Lattice Boltzmann program. ![C++](logo/cpp.svg) - [lanl/MF-LBM](https://github.com/lanl/MF-LBM) - A Portable, Scalable and High-performance Lattice Boltzmann Code for DNS of Flow in Porous Media. ![FORTRAN](logo/FORTRAN.svg) ### Design and Optimization - [mdolab/dafoam](https://github.com/mdolab/dafoam) - DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Gradient-based Design Optimization. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [peterdsharpe/AeroSandbox](https://github.com/peterdsharpe/AeroSandbox) - Aircraft design optimization made fast through modern automatic differentiation. ![Python](logo/Python.svg) - [DARcorporation/xfoil-python](https://github.com/DARcorporation/xfoil-python) - Stripped down version of XFOIL as compiled python module. ![Python](logo/Python.svg) ### Coupling - [precice/precice](https://github.com/precice/precice) - A coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations. ![C++](logo/cpp.svg) ### Chemical Kinetics - [Cantera/cantera](https://github.com/Cantera/cantera) - Chemical kinetics, thermodynamics, and transport tool suite. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) ### Supersonic / Hypersonic Flow - [matteobernardini/STREAmS](https://github.com/matteobernardini/STREAmS) - STREAmS performs Direct Numerical Simulations of compressible turbulent flows in Cartesian geometry solving the unsteady, fully compressible Navier-Stokes equations for a perfect gas. ![FORTRAN](logo/FORTRAN.svg) - [sergeas67/OpenHyperFLOW2D](https://github.com/sergeas67/OpenHyperFLOW2D) - 2D (flat/axisymmetrical) transient viscous compressible multicomponent sub/trans/supersonic reacting gas flow with RANS/URANS turbulence models. ![C++](logo/cpp.svg) ### Differential programming - [taichi-dev/taichi](https://github.com/taichi-dev/taichi) - Parallel programming for everyone. ![Python](logo/Python.svg) - [tum-pbs/PhiFlow](https://github.com/tum-pbs/PhiFlow) - A differentiable PDE solving framework for machine learning. ![Python](logo/Python.svg) - [google/jax-cfd](https://github.com/google/jax-cfd) - Computational Fluid Dynamics in JAX. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/google/jax-cfd/search?l=jupyter-notebook) ### Neural Networks for PDE - [lululxvi/deepxde](https://github.com/lululxvi/deepxde) - Deep learning library for solving differential equations and more. ![Python](logo/Python.svg) - [SciML/NeuralPDE.jl](https://github.com/SciML/NeuralPDE.jl) - Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation. ![julia](logo/julia.svg) - [google/neural-tangents](https://github.com/google/neural-tangents) - Fast and Easy Infinite Neural Networks in Python. ![Python](logo/Python.svg) - [isl-org/DeepLagrangianFluids](https://github.com/isl-org/DeepLagrangianFluids) - Lagrangian Fluid Simulation with Continuous Convolutions. ![Python](logo/Python.svg) - [maxjiang93/space_time_pde](https://github.com/maxjiang93/space_time_pde) - MeshfreeFlowNet: Physical Constrained Space Time Super-Resolution. ![Python](logo/Python.svg) ### Graphics - [PavelDoGreat/WebGL-Fluid-Simulation](https://github.com/PavelDoGreat/WebGL-Fluid-Simulation) - Play with fluids in your browser (works even on mobile). ![JavaScript](logo/JavaScript.svg) - [doyubkim/fluid-engine-dev](https://github.com/doyubkim/fluid-engine-dev) - Fluid simulation engine for computer graphics applications. ![C++](logo/cpp.svg) ### Other Techniques - [PyFR/PyFR](https://github.com/PyFR/PyFR) - Framework for solving advection-diffusion type problems on streaming architectures using the Flux Reconstruction approach of Huynh. ![Python](logo/Python.svg) - [pencil-code/pencil-code](https://github.com/pencil-code/pencil-code) - A high-order finite-difference code for compressible hydrodynamic flows with magnetic fields and particles. ![FORTRAN](logo/FORTRAN.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/pencil-code/pencil-code/search?l=jupyter-notebook) - [NaluCFD/Nalu](https://github.com/NaluCFD/Nalu) - Nalu: a generalized unstructured massively parallel low Mach flow code designed to support a variety of open applications of interest built on the Sierra Toolkit and Trilinos solver Tpetra solver stack. ![C++](logo/cpp.svg) - [lesgo-jhu/lesgo](https://github.com/lesgo-jhu/lesgo) - The Large-Eddy Simulation framework from the Turbulence Research Group at Johns Hopkins University. ![FORTRAN](logo/FORTRAN.svg) - [cornellius-gp/gpytorch](https://github.com/cornellius-gp/gpytorch) - A highly efficient and modular implementation of Gaussian Processes in PyTorch. ![Python](logo/Python.svg) ## Experimental Fluid Dynamics ### PIV / PTV - [JHU-NI-LAB/OpenLPT_Shake-The-Box](https://github.com/JHU-NI-LAB/OpenLPT_Shake-The-Box) - Open-source C++ code for Shake-the-box, particle tracking algorithm. ![C++](logo/cpp.svg) ![MATLAB](logo/MATLAB.svg) - [OpenPTV/openptv](https://github.com/openptv/openptv) - OpenPTV - open source 3D-PTV software. ![C++](logo/cpp.svg) - [OpenPIV/openpiv-python](https://github.com/openpiv/openpiv-python) - OpenPIV consists in a Python and Cython modules for scripting and executing the analysis of a set of PIV image pairs. ![Python](logo/Python.svg) ![MATLAB](logo/MATLAB.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/OpenPIV/openpiv-python/search?l=jupyter-notebook) - [OpenPTV/pyptv](https://github.com/openptv/pyptv) - Python GUI for OpenPTV - open source three-dimensional particle tracking velocimetry. ![Python](logo/Python.svg) - [ronshnapp/MyPTV](https://github.com/ronshnapp/myptv) - Python based 3D-PTV - open source pure Python three-dimensional particle tracking velocimetry. ![Python](logo/Python.svg) ### ML / Optical Flow - [erizmr/UnLiteFlowNet-PIV](https://github.com/erizmr/UnLiteFlowNet-PIV) - Unsupervised learning of Particle Image Velocimetry. (ISC 2020). ![Python](logo/Python.svg) - [shengzesnail/PIV-LiteFlowNet-en](https://github.com/shengzesnail/PIV-LiteFlowNet-en) - Particle image velocimetry via a deep neural network (LiteFlowNet). ![C++](logo/cpp.svg) ![Python](logo/Python.svg) ![MATLAB](logo/MATLAB.svg) - [yongleex/PIV-DCNN](https://github.com/yongleex/PIV-DCNN) - Perform PIV image pair match using deep conv neural network. ![MATLAB](logo/MATLAB.svg) ![C++](logo/cpp.svg) ## Post-processing and Data Analysis - [numpy/numpy](https://github.com/numpy/numpy) - The fundamental package for scientific computing with Python. ![Python](logo/Python.svg) - [mathLab/PyDMD](https://github.com/mathLab/PyDMD) - Python Dynamic Mode Decomposition. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/mathLab/PyDMD/search?l=jupyter-notebook) - [dynamicslab/pysindy](https://github.com/dynamicslab/pysindy) - A sparse regression package with several implementations for the Sparse Identification of Nonlinear Dynamical systems. ![Python](logo/Python.svg) - [ritchieng/eigenvectors-from-eigenvalues](https://github.com/ritchieng/eigenvectors-from-eigenvalues) - This repository implements a calculation of eigenvectors from eigenvectors elegantly through PyTorch. ![Jupyter](logo/Jupyter.svg) - [mengaldo/PySPOD](https://github.com/mengaldo/PySPOD) - A Python package for spectral proper orthogonal decomposition (SPOD). ![Python](logo/Python.svg) - [belson17/modred](https://github.com/belson17/modred) - An easy-to-use and parallelized library for finding modal decompositions and reduced-order models. ![Python](logo/Python.svg) - [Astroua/TurbuStat](https://github.com/Astroua/TurbuStat) - Statistics of Turbulence Python Package. ![Python](logo/Python.svg) - [SURGroup/UQpy](https://github.com/SURGroup/UQpy) - UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/SURGroup/UQpy/search?l=jupyter-notebook) - [haller-group/LCStool](https://github.com/haller-group/LCStool) - LCStool: LCS Tool is a computational engine for analyzing fluid flows by extracting their most influential material surfaces, Lagrangian Coherent Structures. ![MATLAB](logo/MATLAB.svg) ## Visualization ### 2D Visualization - [matplotlib/matplotlib](https://github.com/matplotlib/matplotlib) - matplotlib: plotting with Python. ![Python](logo/Python.svg) - [scikit-image/scikit-image](https://github.com/scikit-image/scikit-image) - Image processing in Python. ![Python](logo/Python.svg) - [3b1b/manim](https://github.com/3b1b/manim) - Animation engine for explanatory math videos. ![Python](logo/Python.svg) - [garrettj403/SciencePlots](https://github.com/garrettj403/SciencePlots) - Matplotlib styles for scientific plotting. ![Python](logo/Python.svg) - [mwaskom/seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization in Python. ![Python](logo/Python.svg) - [lukelbd/proplot](https://github.com/lukelbd/proplot) - :art: A succinct matplotlib wrapper for making beautiful, publication-quality graphics. ![Python](logo/Python.svg) ### 3D Visualization - [K3D-tools/K3D-jupyter](https://github.com/K3D-tools/K3D-jupyter) - K3D lets you create 3D plots backed by WebGL with high-level API (surfaces, isosurfaces, voxels, mesh, cloud points, vtk objects, volume renderer, colormaps, etc). ![Python](logo/Python.svg) ![JavaScript](logo/JavaScript.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/K3D-tools/K3D-jupyter/search?l=jupyter-notebook) - [sciapp/gr](https://github.com/sciapp/gr) - GR framework: a graphics library for visualisation applications. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) - [QuantStack/ipygany](https://github.com/QuantStack/ipygany) - 3-D Scientific Visualization in the Jupyter Notebook. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/QuantStack/ipygany/search?l=jupyter-notebook) - [InsightSoftwareConsortium/itkwidgets](https://github.com/InsightSoftwareConsortium/itkwidgets) - Interactive Jupyter widgets to visualize images, point sets, and meshes in 2D and 3D. ![Python](logo/Python.svg) ![JavaScript](logo/JavaScript.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/InsightSoftwareConsortium/itkwidgets/search?l=jupyter-notebook) - [NVIDIA/ipyparaview](https://github.com/NVIDIA/ipyparaview) - iPython widget for server-side ParaView rendering in Jupyter. ![Python](logo/Python.svg) ![JavaScript](logo/JavaScript.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/NVIDIA/ipyparaview/search?l=jupyter-notebook) - [Kitware/Vtk](https://gitlab.kitware.com/vtk/vtk) - Visualization Toolkit. ![C++](logo/cpp.svg) - [Kitware/paraview](https://www.paraview.org/) - An open-source, multi-platform data analysis and visualization application. ![Python](logo/Python.svg) - [pyvista/pyvista](https://github.com/pyvista/pyvista) - 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). ![Python](logo/Python.svg) ## Benchmarks and Datasets ### Datasets - [shengzesnail/PIV_dataset](https://github.com/shengzesnail/PIV_dataset) - PIV dataset. ![MATLAB](logo/MATLAB.svg) - [idies/pyJHTDB](https://github.com/idies/pyJHTDB) - Python wrapper for the Johns Hopkins turbulence database library. ![Python](logo/Python.svg) [![Jupyter](logo/Jupyter.svg)](https://github.com/idies/pyJHTDB/search?l=jupyter-notebook) ### Benchmarks - [dionhaefner/pyhpc-benchmarks](https://github.com/dionhaefner/pyhpc-benchmarks) - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python. ![Python](logo/Python.svg) - [su2code/TestCases](https://github.com/su2code/TestCases) - An extensive collection of common test cases for SU2. ![C++](logo/cpp.svg) ![Python](logo/Python.svg) ## Reproducibility - [iterative/dvc](https://github.com/iterative/dvc) - Data Version Control, Git for Data & Models, ML Experiments Management. ![Python](logo/Python.svg) - [sphinx-doc/sphinx](https://github.com/sphinx-doc/sphinx) - Sphinx is a tool that makes it easy to create intelligent and beautiful documentation for Python projects. ![Python](logo/Python.svg) ## Community - [CFD Online Forum](https://www.cfd-online.com/Forums/) - A free community for everyone interested in Computational Fluid Dynamics. - [r/CFD](https://www.reddit.com/r/CFD/) - Reddit community on *Computational Fluid Dynamics*. ![r/CFD](https://img.shields.io/reddit/subreddit-subscribers/cfd?label=r%2FCFD&style=flat-square&labelColor=black) - [r/FluidMechanics](https://www.reddit.com/r/FluidMechanics/) - Reddit community on *Fluid Mechanics*. ![r/FluidMechanics](https://img.shields.io/reddit/subreddit-subscribers/FluidMechanics?label=r%2FFluidMechanics&style=flat-square&labelColor=black) ## Related Topics - [alexlib/awesome_piv](https://github.com/alexlib/awesome_piv) - A curated list of repositories related to PIV (particle image velocimetry). ![Awesome](logo/awesome.svg) - [nschloe/awesome-scientific-computing](https://github.com/nschloe/awesome-scientific-computing) - Curated list of awesome software for numerical analysis and scientific computing. ![Awesome](logo/awesome.svg) - [qd-cae/awesome-CAE](https://github.com/qd-cae/awesome-CAE) - A curated list of awesome CAE frameworks, libraries and software. ![Awesome](logo/awesome.svg) - [awesomedata/awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets) - A topic-centric list of HQ open datasets. ![Awesome](logo/awesome.svg)
Last changed by  
 
noisyoscillator
834

Read more

Gaussian process inference of stochastic differential equations

Approximate Gaussian process inference for the drift function in stochastic differential equations Phys. Rev. E 98, 022109 (2018) - Approximate Bayes learning of stochastic differential equations (aps.org) Gaussian Process Approximations of Stochastic Differential Equations (mlr.press) Moment-Based Variational Inference for Stochastic Differential Equations (mlr.press) Variational Inference for Stochastic Differential Equations - Opper - 2019 - Annalen der Physik - Wiley Online Library Sparse Gaussian Processes for Stochastic Differential Equations | OpenReview

5/10/2022
Shapash

🎉 What's new ? :warning: Starting from Shapash v2.0.0: '.compile()' parameters must be provided in the SmartExplainer init: :warning: For clearer initiation, method's parameters must be provided at the init: xpl = SmartExplainer(model, backend, preprocessing, postprocessing, features_groups) instead of xpl.compile(x, model, backend, preprocessing, postprocessing, features_groups) Version New Feature Description

5/7/2022
Projection Operators and Memory Function Formalism

Books Zwanzig-Nonequilibrium Statistical Mechanics Memory Functions, Projection Operators, and the Defect Technique (Lecture Notes in Physics) (Advances in Chemical Physics) - Memory Function Approahes to Stochastic Problems in Condensed Matter, Volume 62(1985). Boon, Yip - Molecular Hydrodynamics (Chapter 1, 2) Projection Operator Techniques in the theory of fluctuations by Bruce Berne in Modern Theoretical Chemistry. v.5 Statistical Mechanics, Part B Time-dependent processes Dieter Forster.-Hydrodynamic fluctuations, broken symmetry, and correlation functions Chapter 11 in Bruce J. Berne, Robert Pecora - Dynamic Light Scattering

5/7/2022
Stochastic fast-slow systems

The evolution of a physical system goverened by a nonlinear differential equation often occurs on two time scales when the parameters affecting the dynamics is small or large. This separation of time scales vastly simplifies their study and is a fundamental requirement to all sorts of perturbation techniques in the parameter space that one could employ. The subclass of linear systems on the hand can be readily generalized to the case of linear partial differential equations like the Fokker-Planck or forward Kolmogorov equations, Euler/Burgers equation using Mori-Zwanzig's projection operator formalism which has the effect integrating out the fast degrees of freedom of the system (e.g. solvent) and projecting this averaging effect onto the slow variables (e.g. time evolution of the spatial coordinate of a protein molecule) whose dyamics now acquires a memory kernel plus noise. The appearance of the memory kernel makes the overall dynamics non-Markovian. The projection operator formalism is a very powerful technique which leads to reduced order modelling of the system where one only tracks (or models) the dynamics of the slow variables with clear separation of two time scales. More details on this formalism can be found in the following excellent presentation: {%youtube e8QFNh5u_1U %} For a detailed introduction to slow-fast dynamical systems take a look at Nils Berglund, Barbara Gentz - Noise-Induced Phenomena in Slow-Fast Dynamical Systems: A Sample-Paths Approach. Many real systems, however, possess a continuum of time scales, with no clear separation. In the multiscale analysis of such systems one is concerned with the bridging of disparate scales by merging different mathematical models appropriate at different scales (such as quantum, molecular, and continuum). Finally one looks for a phenomenological description of the phenomena under study that is valid at all scales using the more powerful renormalization group theory. This paper discusses all these powerful theoretical concepts beautifully.

5/4/2022
Read more from noisyoscillator
Published on HackMD