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

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.

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

Arrays with arbitrarily nested named components.

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Build 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)

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Chemical Reaction Neural Network

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Latent Differential Equations models in Julia.

Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)