Code for Tensorflow Machine Learning Cookbook

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

A 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 collection of resources regarding the interplay between differential equations, dynamical systems, deep learning, control and optimization.

Convert julia objects to LaTeX equations, arrays or other environments.

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

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

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

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML)

Java 3D Physics Engine & Library

Fortran Object-Oriented Differential-equations Integration Environment, FOODIE

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

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

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

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

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

Simulate from ODE-based population PK/PD and QSP models in R

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

Code for the paper "Learning Differential Equations that are Easy to Solve"

Reachability and Safety of Nondeterministic Dynamical Systems

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

Game Engine Framework for the D programming language

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

Mirror of Apache ODE

Bulirsch-Stoer integration of systems of ordinary differential equations in JavaScript

A collection of numerical methods written in Nim