There are 3 repositories under differential-algebraic-equations topic.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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
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
🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.
Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Geometric Numerical Integration in Julia
A simple but powerful header-only C++ DAE (Differential Algebraic Equation) system solver
Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
Manipulation of generalized state-space (descriptor) system representations using Julia
Classic problems in chemical engineering solved with matlab
control theory repo
Modifications of Scipy's implicit solvers for the solution of differential-algebraic equations (DAEs)
Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem
Javascript library for defining and solving DAE equations efficiently using WebAssembly
Projects to model the catalytic combustion of a hydrogen-oxygen system flowing through a 1D plug flow reactor and a 2D rectangular reactor.
This project provides a novel combination of the field of differential algebraic equations and deep neural networks, and this combination enables us to add constraints to neural networks. We explore various constraint methods and compare their strengths and weaknesses.
Half-explicit Runge-Kutta method for overdetermined semi-implicit Differential-Algebraic Equations
Parameter Range Reduction using Monotonic Discretizations
This repository includes numerical integration-based solutions for solving non-linear equations. The solutions include Newton Raphson technique for algebraic equations and Euler's/Modified-Euler's/Runge-Kutta (RK4) for differential algebraic equations.
Time-Limited Balanced Truncation Model Order Reduction for Descriptor Systems
Test Set for Initial Value Problems
COCO constructors for delay-differential-algebraic equations
Passivity-preserving model reduction for descriptor systems via spectral factorization