Blind Dynamical Taxonomy: A Pre-Registered Failure Limits of Feature-Based Classification Across Canonical Dynamical Systems
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901370922074112 |
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| author | Mitchell , Thomas S. |
| author_facet | Mitchell , Thomas S. |
| contents | <p>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.<br>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.<br>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.<br>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20084204 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Blind Dynamical Taxonomy: A Pre-Registered Failure Limits of Feature-Based Classification Across Canonical Dynamical Systems Mitchell , Thomas S. 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 <p>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.<br>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.<br>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.<br>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.</p> |
| title | Blind Dynamical Taxonomy: A Pre-Registered Failure Limits of Feature-Based Classification Across Canonical Dynamical Systems |
| topic | 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 |
| url | https://doi.org/10.5281/zenodo.20084204 |