Differentialequations.jlMulti-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components
velodynDynamical systems methods for RNA velocity analysis
mbsimA multi-body simulation software
sssMORsssMOR - Sparse State-Space and Model Order Reduction Toolbox
egtplotegtplot: A python package for 3-Strategy Evolutionary Games
PyRatesOpen-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.
pompR package for statistical inference using partially observed Markov processes
FactorGraph.jlThe FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.
chaotic-mapsSimple implementations of chaotic maps in Processing
RaPIdRaPId (a recursive acronym for "Rapid Parameter Identification") utilizes different optimization and simulation technologies to provide a framework for model validation and calibration of any kind of dynamical systems, but specifically catered to power systems.
codacCodac is a library for constraint programming over reals, trajectories and sets.
sysidentpyA Python Package For System Identification Using NARMAX Models
RigidBodySim.jlSimulation and visualization of articulated rigid body systems in Julia
PySPODA Python package for spectral proper orthogonal decomposition (SPOD).
MongeAmpereFlowContinuous-time gradient flow for generative modeling and variational inference
eomConfigurable ODE/PDE solver
BrainPyBrain Dynamics Programming in Python
SDEToolsMatlab Toolbox for the Numerical Solution of Stochastic Differential Equations
radCADA framework for generalised dynamical systems modelling & simulation (inspired by and compatible with cadCAD.org)
pressioModel reduction for linear and nonlinear dynamical systems: core C++ library
Pontryagin-Differentiable-ProgrammingA unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.