Open Source MATLAB Simulation Software

The General Mission Analysis Tool (GMAT) is an open-source tool for space mission design and navigation. GMAT is developed by a team of NASA, private industry, and public and private contributors. The GMAT development team is pleased to announce the release of GMAT version R2025a. For a complete list of new features, compatibility changes, and bug fixes, see the R2025a Release Notes in the Users Guide.

A package for transient and steady state simulation of organic solar cells.

A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.

ADiGator is a source transformation via operator overloading tool for the automatic differentiation of mathematical functions written in MATLAB. Given a user written file, together with information on the inputs of said file, ADiGator uses forward mode automatic differentiation to generate a new file which contains the calculations required to compute the numeric derivatives of the original user function. Furthermore, these calculations are written entirely in the native MATLAB language, and thus the process may be repeated to obtain nth order derivative files. The package is particularly appealing for applications where the same derivative must be found at multiple different points, i.e. non-linear root finding/optimization, stiff ode integration, etc.

The Free Finite Element Package is a library which contains numerical methods required when working with finite elements. The goal of FFEP is to provide basic functions for approximating the solution of elliptic and parabolic PDEs in 2D. Until 2016 FFEP was developed using C. Science 2016 the language was switched to GNU Octave with some C Mex-Functions. Mesh generation or import is not part of FFEP. We recommend the use of Gmsh.

A collection of different methods for implementing in C++ polycrystalline growth for metals during annealing or recrystallization.

RYUON : many-particle simulation suite

A MATLAB package to simulate sample paths of the solution of a Itô or Stratonovich stochastic differential equation (SDE), compute statistics and estimate the parameters from data. A note of caution: SDE Toolbox is no more developed but it's still downloadable. Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.

A MATLAB toolbox for time series analysis using state space models. Supports fully interactive model construction with MATLAB objects and efficient Kalman filter backend implemented in c.

A MATLAB toolbox for approximate Bayesian computation (ABC) in stochastic differential equation models. It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the "state-space" modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. Variance components for the "measurement error" affecting the data/observations can be estimated. A 50-pages Reference Manual is provided with two case-studies implemented and discussed. The methodology is based on the research article available at http://arxiv.org/abs/1204.5459 Author's research page is http://www.maths.lth.se/matstat/staff/umberto/

Anmech is a tool for symbolic calculations and mathematical modelling using Matlab. Its main functions are automated derivation of equations of motion of mechanical systems and derivation of system of differential equations with numeric parameters.

Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples. It is also useful as way of exploring surprise theory. For more information see also: http://ilab.usc.edu/

Blueprint XAS is a Matlab-based suite designed for the processing and analysis of near-edge x-ray absorption spectroscopy (XAS) data. The suite is designed primarily to assist users in exploring reasonable fit solutions while minimizing user bias.

This project hosts the code and the documents which were created by Francesco Montorsi during his PhD at the University of Modena and Reggio Emilia, Italy, about indoor and outdoor localization and navigation techniques. The code (mostly MATLAB code) is publicly available to enable reproducible research.

Gaussian Process model for fitting deterministic simulator output. Establish efficient and reliable likelihood optimization through hybridized DIRECT-BFGS and multi-start BFGS algorithms. Programming Language: Matlab.

A library of data objects that simulate mathematical neurons, including classic Hodgkin-Huxley neurons and theta neurons.

math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net

gfit creates an interface between computational models and experimental data and provides tools for their analysis

Octave interface to the finite elements library Libmesh. Under the scientific interpreted language Octave, Octmesh allows using the Libmesh characteristics for the numerical simulation of partial differential equations on serial and parallel platforms.