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

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 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.

R package for statistical inference using partially observed Markov processes

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

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

EPDE - partial differential equations discovery framework

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

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

Simulaciones numéricas en módelos de ecuaciones diferenciales

Software for contour extraction and mesh generation for numerical analysis

This repository provides essential numerical algorithms for solving mathematical problems. Covering linear equations, differential equations and more, it's a valuable resource for students and professionals in science and engineering.

A standard library of components to model the world and beyond

Telegram bot that solves differential equations of the 1st and 2nd order using the Runge-Kutta method

Brian is a free, open source simulator for spiking neural networks.

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

V library to develop Artificial Intelligence and High-Performance Scientific Computations

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

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