jbramburger/Deep-Conjugacies
Code associated to the paper "Deep learning of conjugate mappings"
This project helps researchers and scientists understand complex, chaotic dynamical systems by creating simpler, 'conjugate' representations. It takes observational data from continuous-time systems (like Rossler or Lorenz systems) and outputs a simplified mathematical mapping that makes the underlying chaos easier to interpret and classify. This tool is for physicists, mathematicians, and engineers who work with nonlinear dynamics and need to analyze or control chaotic behaviors.
No commits in the last 6 months.
Use this if you need to analyze the chaotic behavior of a continuous dynamical system and want an explicit, simplified model (a Poincare map) to understand its underlying dynamics.
Not ideal if you are working with simple, predictable systems or if your primary goal is not the deep analysis of chaotic system topologies.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 17, 2021
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