stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Catalyst
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
sb3-contrib
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
ModelingToolkit
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
SciMLBenchmarks
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
JumpProcesses
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)
DiffEqBase
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
StochasticDiffEq
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
SciMLSensitivity
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.
DifferentialEquations
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.
DiffEqNoiseProcess
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
SteadyStateDiffEq
Solvers for steady states in scientific machine learning (SciML)
SciMLTutorials
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
DiffEqParamEstim
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
MultiScaleArrays
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
DiffEqBayes
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
DiffEqDevTools
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
DiffEqFinancial
Differential equation problem specifications and scientific machine learning for common financial models
DiffEqCallbacks
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
BVProblemLibrary
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
diffeqdocs.jl
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
BridgeDiffEq
A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
pydaddy
Python package to discover stochastic differential equations from time series data
BoundaryCrossingProbabilities
Computes the boundary crossing probability for a general diffusion process and time-dependent boundary.
sdevelo
Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo
manifold-mcmc-for-diffusions
Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'
SciMLWorkshop
Workshop materials for training in scientific computing and scientific machine learning