Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

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

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

A curated list of awesome Windows frameworks, libraries, software and resources for Red Teams

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

File manager for deepin desktop environment.

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.

A collection of resources for pulling real-time streaming data off of TDAmeritrade's ThinkOrSwim(TOS) platform; providing C, C , Java and Python interfaces.

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

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

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

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

The Base interface of the SciML ecosystem

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

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

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

Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.

A light and fast internet speed plugin（DDE）.

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

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.