Table des matières: :: Library Catalog

Enregistré dans:

Détails bibliographiques
Auteur principal:	Mitchell , Thomas S.
Format:	Recurso digital
Langue:	anglais
Publié:	Zenodo 2026
Sujets:	dynamical systems, time-series analysis, feature extraction, clustering, reaction-diffusion systems, Gray-Scott model, Kuramoto model, Ising model, recurrence quantification analysis, attractor reconstruction, temporal geometry, Keller-ΔΦ, nonlinear dynamics, complexity science, observability, embedding space, phase transitions, topology, dynamical taxonomy, negative results
Accès en ligne:	https://doi.org/10.5281/zenodo.20084204
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Table des matières:

Pre-registered negative-results study testing whether canonical dynamical systems can be classified from observable time-series features alone. Twenty-one systems spanning seven dynamical families were embedded using 22 extracted features across spectral, temporal, recurrence-quantification, transient-response, morphology, and embedding domains. The experiment failed its pre-registered success threshold. The strongest failure occurred within the Gray-Scott reaction-diffusion family itself, where systems generated by the same governing PDE failed to cluster together under parameter variation. Results demonstrate that parameter variation within a fixed mechanism family can overwhelm observable feature similarity in low-dimensional embedding space. A companion naive-feature experiment produced complete collapse into a single dominant cluster. The paper discusses why feature-based dynamical taxonomy fails under projection loss, observable dependence, and parameter sensitivity, and outlines what types of invariant representations may be required for robust classification.

Documents similaires