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

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

Python library for arbitrary-precision floating-point arithmetic

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, 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

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

VIP cheatsheets for Stanford's CME 102 Ordinary Differential Equations for Engineers

Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support

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 and Python examples from my book on using PETSc and Firedrake to solve PDEs

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

The Base interface of the SciML ecosystem

Python notebooks for Numerical Analysis

AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.

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

A standard library of components to model the world and beyond

Solve ODEs fast, with support for PyMC