SciML Open Source Scientific Machine Learning
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- Fast and automatic structural identifiability software for ODE systems
- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- A standard library of components to model the world and beyond
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- 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
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.- 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
- Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
SciMLBook
PublicParallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)SciMLDocs
PublicGlobal documentation for the Julia SciML Scientific Machine Learning OrganizationQuasiMonteCarlo.jl
PublicLightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)DataInterpolations.jl
Public- The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
- 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.
HighDimPDE.jl
PublicA Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality- Surrogate modeling and optimization for scientific machine learning (SciML)
NeuralPDE.jl
PublicPhysics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulationDiffEqGPU.jl
PublicGPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
PoissonRandom.jl
PublicFast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)EllipsisNotation.jl
PublicComponentArrays.jl
PublicArrays with arbitrarily nested named components.SciMLBase.jl
PublicThe Base interface of the SciML ecosystemStochasticDelayDiffEq.jl
PublicStochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystemNeuralLyapunov.jl
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