SciML Open Source Scientific Machine Learning
Websites:Organization Website |Documentation
The SciML organization is a collection of tools for solving equations and modeling systemsdeveloped in the Julia programming language with bindings to other languages such as R andPython. The organization provides well-maintained tools which compose together as acoherent ecosystem. It has a coherent development principle, unified APIs over largecollections of equation solvers, pervasive differentiability and sensitivity analysis, andfeatures many of the highest performance and parallel implementations one can find.
Scientific Machine Learning (SciML) = Scientific Computing + Machine Learning
- Want to get started running some code? Check out theGetting Started tutorials.
- What is SciML? Check out ourOverview.
- Want to see some cool end-to-end examples? Check out theSciML Showcase.
- Want to learn more about how SciML does scientific machine learning? Check out theSciML Book (from MIT's 18.337 graduate course).
- Curious about our performance claims? Check outthe SciML Open Benchmarks.
- Want to chat with someone? Check outour chat room andforums.
- Want to see our code? Check outthe SciML Github organization.
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- DifferentialEquations.jl
DifferentialEquations.jl PublicMulti-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
- ModelingToolkit.jl
ModelingToolkit.jl PublicAn 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…
- NeuralPDE.jl
NeuralPDE.jl PublicPhysics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- DiffEqFlux.jl
DiffEqFlux.jl PublicPre-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
Repositories
- 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.
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SciML/LinearSolve.jl’s past year of commit activity - SciMLBenchmarksOutput Public
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
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SciML/SciMLBenchmarksOutput’s past year of commit activity - RuntimeGeneratedFunctions.jl Public
Functions generated at runtime without world-age issues or overhead
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SciML/RuntimeGeneratedFunctions.jl’s past year of commit activity - SciMLBenchmarks.jl Public
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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SciML/SciMLBenchmarks.jl’s past year of commit activity - PoissonRandom.jl Public
Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
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SciML/EllipsisNotation.jl’s past year of commit activity - SciMLBook Public
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
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SciML/DataInterpolations.jl’s past year of commit activity - Optimization.jl Public
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
SciML/Optimization.jl’s past year of commit activity - OrdinaryDiffEqOperatorSplitting.jl Public
Toolbox to handle and solve split formulations of a wide variety of ODE and DAE problems.
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SciML/OrdinaryDiffEqOperatorSplitting.jl’s past year of commit activity
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