PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

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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.

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

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.

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

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

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.

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

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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)

New home of Swift Development Environment for VS Code

The Base interface of the SciML ecosystem

A standard library of components to model the world and beyond

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)

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning 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 collection of a number of design patterns and principles written in Kotlin

A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.

Matlab Toolbox for the Numerical Solution of Stochastic Differential Equations

Solving linear, nonlinear equations, ordinary differential equations, ... using numerical methods in fortran

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

Python package to discover stochastic differential equations from time series data