🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The SciML Scientific Machine Learning Software Organization Website
Number Type Converter, online three.js based 3D viewers: 3DM, 3DS, 3MF, AMF, ASSIMP, BIM, BREP, DAE, DST, FBX, GCODE, GLTF, IFC, IGES, JSON, LDRAW, MMD, OBJ, PCD, PDB, PLY, PRWM, SPLAT, STEP, STL, USDZ, VRML, VTK, XYZ and Texture Viewer for PCX, PNG, JPG, BMP, GIF, TIFF, TGA, SVG, WEBP, DDS, KTX(2), EXR, HDR, BASIS, Lottie JSON. Some AR / VR.
eBPF-based Linux high-performance transparent proxy solution.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
The Base interface of the SciML ecosystem
The official Open-Asset-Importer-Library Repository. Loads 40+ 3D-file-formats into one unified and clean data structure.
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Chnroutes rules for routers、Shadowrocket、Quantumult、acl、v2rayNG、v2rayN、pac、NekoRay、Nekobox、v2rayA、dae、RouterOS、sing-box、v2ray config file.
A simulation modelling language
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
A PyTorch Lightning-based library for self- and semi-supervised learning on tabular data.
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
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