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

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

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

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

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.

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

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 standard library of components to model the world and beyond

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.

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.

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

Sniper shooting simulator in 2D where bullet motion depends on rifle geometry, bullet speed, shooting angle, gravity and potentially air drag and wind forces