There are 22 repositories under dynamical-systems 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.
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
A package for the sparse identification of nonlinear dynamical systems from data
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
A Control Systems Toolbox for Julia
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
Python package for solving partial differential equations using finite differences.
A Python Package For System Identification Using NARMAX Models
Arrays with arbitrarily nested named components.
Code for the paper "Learning Differential Equations that are Easy to Solve"
Nonlinear Dynamics: A concise introduction interlaced with code
Tools for the exploration of chaos and nonlinear dynamics
Computing reachable states of dynamical systems in Julia
Fast, general-purpose interactive applications for complex systems
Simulate dynamic systems expressed in block diagram form using Python
Source code for 'Dynamical Systems with Applications Using Python' by Stephen Lynch
A 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.
Block diagram editor and real time code generator for Python
System Identification toolbox, compatible with ControlSystems.jl
Firmware, hardware and documentation for my autonomous quad copter project
State estimation, smoothing and parameter estimation using Kalman and particle filters.
Causal.jl - A modeling and simulation framework adopting causal modeling approach.