Analyze chaotic dynamical systems using Lyapunov exponents and bifurcation diagrams.
npx skills add https://github.com/GuitarAlchemist/ix --skill ix-chaos
Please help me install this Agent Skill. Skill: ix-chaos Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-chaos Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-chaos
This Skill enables the analysis of chaotic behavior in dynamical systems, helping users understand complex system dynamics.
Analyze the chaos in the logistic map x_{n+1} = r * x_n * (1 - x_n) by providing the parameter 'r' and time series data.
High-intent search queries and answers about installing and using this skill.
To calculate Lyapunov exponents for a dynamical system, you analyze time series data to measure the rate of separation of infinitesitimally close trajectories. This Skill computes these exponents to directly distinguish between chaotic, periodic, and stable behaviors.
The best way to reconstruct strange attractors from scalar time series data is using delay embedding. This Skill applies delay embedding techniques to accurately reconstruct the underlying attractor dynamics from your one-dimensional observations.
Yes, you can use delay embedding to reconstruct attractors from scalar time series data and Poincaré sections to simplify continuous dynamics into discrete maps. This Skill integrates both methods to facilitate comprehensive continuous time series analysis.
To generate bifurcation diagrams for the logistic map, you provide the varying parameter 'r' and time series data. This Skill visualizes how system behavior changes across varying parameter values to reveal structural transitions.
Available chaos control methods include the OGY method and Pyragas time-delay feedback. This Skill applies these specific techniques to stabilize and control chaotic dynamical systems effectively.
You should estimate fractal dimensions when you need quantitative insights into a system's complexity beyond visual plots. This Skill estimates fractal dimensions to measure the geometric complexity of strange attractors like Lorenz or Rössler systems.
pengqianhanIdentify sparse governing equations for dynamical systems from time-series data using PySINDy.
Supporter09Generate publication-quality ML paper figures with matplotlib and seaborn.
garochee33Generate fractal structures and measure self-similar complexity from recursive rules.
tassiovaleFit linear regression and time series models with Python's statsmodels library.
