A standard library of components to model the world and beyond

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

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

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

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

Physics-Informed Neural networks for Advanced modeling

Rust Scientific Libary. ODE and DAE (Runge-Kutta) solvers. Special functions (Bessel, Elliptic, Beta, Gamma, Erf). Linear algebra. Sparse solvers (MUMPS, UMFPACK). Probability distributions. Tensor calculus.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

A simple but powerful header-only C++ DAE (Differential Algebraic Equation) system solver

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

This project involves analyzing the stationary solutions of a system of differential equations depending on the parameter p4 .

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

Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.

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

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

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 collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)

This portfolio contains study cases of different topics, from 📊 operational research, 🔧 mechanical design, 📈 differential equations, 🌊 fluids mechanics and more.