SciML Open Source Scientific Machine Learning
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- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
- Global documentation for the Julia SciML Scientific Machine Learning Organization
sciml.ai
PublicThe SciML Scientific Machine Learning Software Organization Website- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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 and automated transformations of differential equations- Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
- A common solve function for scientific machine learning (SciML) and beyond
JumpProcesses.jl
PublicBuild and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
DelayDiffEq.jl
PublicDelay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
DiffEqBase.jl
PublicThe lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems- Automatic Finite Difference PDE solving with Julia SciML
- 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.
- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
- The Base interface of the SciML ecosystem
ReservoirComputing.jl
PublicReservoir computing utilities for scientific machine learning (SciML)StochasticDelayDiffEq.jl
PublicStochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem- Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
LinearSolve.jl
PublicLinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.- Boundary value problem (BVP) solvers for scientific machine learning (SciML)
DiffEqDocs.jl
PublicDocumentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystemSciMLBenchmarksOutput
PublicSciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI PerformanceModelOrderReduction.jl
PublicHigh-level model-order reduction to automate the acceleration of large-scale simulations