Popular GitHub Repositories for Ode

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

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.

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

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

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

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

The SciML Scientific Machine Learning Software Organization Website

A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

Workshop materials for training in scientific computing and scientific machine learning

Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization

[TGRS21] Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

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

SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations

Reinforcement learning for batch bioprocess optimization (Computers & Chemical Engineering, 2020)

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn

An interactive web application for gradient matching methods.