There are 13 repositories under nonlinear-dynamics topic.
A package for the sparse identification of nonlinear dynamical systems from data
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
Nonlinear Dynamics: A concise introduction interlaced with code
A Python module implementing some standard algorithms used in nonlinear time series analysis
nonlinear control optimization tool
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
An open source model predictive control package for Julia.
Estimators for probabilities, entropies, and other complexity measures derived from data in the context of nonlinear dynamics and complex systems
A Julia package for solving nonlinear differential equations using the harmonic balance method.
MATLAB code for estimating parameters for phase space reconstruction of multivariate data.
A basic nonlinear model predictive control implementation using Casadi with Unscented Kalman filter state estimation
Recurrence Quantification Analysis in Julia
A suite of tools for solving Nonlinear Schrodinger equations via higher-order algorithms and Darboux transformations.
Nonlinear time series analysis in R
Data-driven reduced order modeling for nonlinear dynamical systems
PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Tutorials on Control Theory
Delay coordinates embeddings and optimizing them
A set of Matlab/Octave files that performs a method of Nonlinear System Identification.
A Master of Engineering Academic Project
Multiscale Modelling Tool - mathematical modelling without the maths
Transition Indicators / Early Warning Signals / Regime Shifts / Change Point Detection
Paper reading list
SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations
MIT Spring 2020 Mechanical Engineering 2.152 Nonlinear Control (Professor Jean-Jacques Slotine)