sde

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

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

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

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

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

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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

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

Benchmarks for scientific machine learning (SciML) software, scientific AI, and (differential) equation solvers

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

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, 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

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

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

New home of Swift Development Environment for VS Code

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

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