Exploring the Topology of Dynamical Reconstructions
Joshua Garland, Elizabeth Bradley, James D. Meiss

TL;DR
This study demonstrates that topological features of dynamical systems can be accurately recovered from noisy scalar data using witness complexes, even with lower-dimensional reconstructions than traditional embedding theorems suggest.
Contribution
It introduces a method to compute homology of dynamical systems without full reconstruction, reducing computational costs and relaxing embedding dimension requirements.
Findings
Witness complexes correctly resolve homology from noisy data.
Homology reconstruction requires less stringent conditions than full dynamical embedding.
Delay-coordinate maps can preserve topological features at lower dimensions.
Abstract
Computing the state-space topology of a dynamical system from scalar data requires accurate reconstruction of those dynamics and construction of an appropriate simplicial complex from the results. The reconstruction process involves a number of free parameters and the computation of homology for a large number of simplices can be expensive. This paper is a study of how to avoid a full (diffeomorphic) reconstruction and how to decrease the computational burden. Using trajectories from the classic Lorenz system, we reconstruct the dynamics using the method of delays, then build a parsimonious simplicial complex---the "witness complex"---to compute its homology. Surprisingly, we find that the witness complex correctly resolves the homology of the underlying invariant set from noisy samples of that set even if the reconstruction dimension is well below the thresholds specified in the…
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