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@SciML

SciML Open Source Scientific Machine Learning

Open source software for scientific machine learning

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Websites: Organization Website | Documentation

SciML Organization Stats: SciML Stars

SciML: Differentiable Modeling and Simulation Combined with Machine Learning

The SciML organization is a collection of tools for solving equations and modeling systems developed in the Julia programming language with bindings to other languages such as R and Python. The organization provides well-maintained tools which compose together as a coherent ecosystem. It has a coherent development principle, unified APIs over large collections of equation solvers, pervasive differentiability and sensitivity analysis, and features many of the highest performance and parallel implementations one can find.

Scientific Machine Learning (SciML) = Scientific Computing + Machine Learning

Where to Start?

Pinned

  1. DifferentialEquations.jl DifferentialEquations.jl Public

    Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…

    Julia 2.8k 221

  2. ModelingToolkit.jl ModelingToolkit.jl Public

    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 a…

    Julia 1.4k 195

  3. DiffEqFlux.jl DiffEqFlux.jl Public

    Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

    Julia 844 151

  4. NeuralPDE.jl NeuralPDE.jl Public

    Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

    Julia 916 194

  5. diffeqpy diffeqpy Public

    Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

    Python 500 39

  6. diffeqr diffeqr Public

    Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

    R 136 14

Repositories

Showing 10 of 170 repositories
  • NonlinearSolve.jl Public

    High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

    Julia 214 MIT 38 30 (8 issues need help) 7 Updated Jun 1, 2024
  • Catalyst.jl Public

    Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

    Julia 427 71 96 37 Updated Jun 1, 2024
  • SciMLSensitivity.jl Public

    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.

    Julia 316 67 86 7 Updated Jun 1, 2024
  • SciMLDocs Public

    Global documentation for the Julia SciML Scientific Machine Learning Organization

    Julia 52 MIT 38 15 1 Updated Jun 1, 2024
  • DiffEqBase.jl Public

    The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

    Julia 299 106 54 15 Updated Jun 1, 2024
  • Julia 6 MIT 2 0 0 Updated Jun 1, 2024
  • NeuralLyapunov.jl Public

    A library for searching for neural Lyapunov functions in Julia.

    Julia 1 1 2 0 Updated May 31, 2024
  • LinearSolve.jl Public

    LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

  • OrdinaryDiffEq.jl Public

    High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

  • ExponentialUtilities.jl Public

    Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.