SciML Open Source Scientific Machine Learning
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- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
- Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
- Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
- 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
- This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
- CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
SciMLBase.jl
PublicThe Base interface of the SciML ecosystem- Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
- A standard library of components to model the world and beyond
SciMLBenchmarksOutput
PublicSciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI PerformanceBaseModelica.jl
PublicNonlinearSolve.jl
PublicHigh-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
diffeqpy
PublicSolving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
BoundaryValueDiffEq.jl
PublicBoundary value problem (BVP) solvers for scientific machine learning (SciML)CommonSolve.jl
PublicA common solve function for scientific machine learning (SciML) and beyond- Arrays with arbitrarily nested named components.
ADTypes.jl
PublicDiffEqCallbacks.jl
PublicA library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solversRecursiveArrayTools.jl
Publicdiffeqr
PublicSolving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystemExponentialUtilities.jl
PublicFast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.