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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

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

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

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

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

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

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

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

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

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

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

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.

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

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

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

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

C++ library for ODE integration via Taylor's method and LLVM

Methods to compute sets of states reachable by dynamical systems