This is our standard library for nonlinear analysis. Many of these functions are the same we use in our services. We do have additional methods that are not public but could be made available in a future release. If you are interested in learning more, attending our workshops or webinars or using our data analysis services please contact bmchnon…

A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.

Non-Linear Dynamic Systems

Python toolbox to detect limit cycles and asses their stability

Official source code of Arrhythmia Detection

Python package to compute Lyapunov exponents, covariant Lyapunov vectors (CLV) and adjoints of a dynamical systems.

Files related to my Summer of Science Report on Nonlinear Dynamics

Calculation and visualization of average maximum lyapunov exponent for a double pendulum system

Evaluate the Lyapunov Spectrum of a dynamical system described by ODEs (in Python)

Repository for STPDP and SSRPC

Coupled linear ODEs to model relationship dynamics

Project for the Quantum Information and Computing course, 2020/2021

Lyapunov Cycle Detector is a collection of algorithms destined to study the basins of attraction of rational maps (that is, the Fatou and Julia sets). In particular, the main focus of LCD is in the detection of attracting n-cycles of said rational map and in computing its basins.

Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory

A Python package to simulate and measure chaotic dynamical systems.

Codigo Fortran que implementa el metodo de Benettin para realizar el calculo de los exponentes de Lyapunov y posteriormente seleccionando un parámetrodell modelo realizar un espectro de los Exponentes de Lyapunov.

Fortran90 examples of Dynamic Systems

Solución numérica de la dinámica de una cosmología de Robertson-Walker, además se puede generar graficas de dicha dinámica, las secciones de Poincare, los exponentes de Lyapunov y las orbitas del sistema.

Bifurcation Diagrams for Discrete-Time Maps in Python - https://doi.org/10.5281/zenodo.13276873

Análisis No Lineal de caoticidad en virus covid-19