ArXiv TLDR

Frustrated Dynamics of Distance Matrices

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2605.05376

Igor Halperin

nlin.PScond-mat.dis-nncond-mat.stat-mechhep-thq-fin.CP

TLDR

Introduces the Frustrated Distance Matrix (FDM) model to track structural changes in particle systems on a sphere using spectral analysis.

Key contributions

  • Introduces Frustrated Distance Matrix (FDM) model for dynamic analysis of particle systems.
  • Demonstrates FDM spectra track structural changes, like particle collapse to a 1D ring.
  • Finds static spectral templates are preserved, with dynamics redistributing spectral mass.
  • Extracts spectral diagnostics from distance matrices to identify structural transitions.

Why it matters

This paper introduces a novel dynamic model (FDM) that extends static spectral analysis to time-evolving systems. It provides a powerful method to detect and characterize structural changes in complex systems solely from distance matrices. The proposed spectral diagnostics have broad applicability across various scientific and engineering domains.

Original Abstract

We introduce the Frustrated Distance Matrix (FDM) model, a dynamic extension of the static distance-matrix ensemble on S^2 analyzed by Bogomolny, Bohigas, and Schmit (BBS). Its entries are pairwise geodesic distances between N Brownian particles on the sphere evolving under quenched random pairwise couplings linear in those distances. Where the static BBS theory recovers geometric information about the underlying manifold from spectra of distance matrices on i.i.d.\ samples, the time-resolved FDM spectrum carries information about structural changes of the underlying point process. The particle dynamics realize one such change: a fast collapse from a uniform configuration onto a one-dimensional ring, followed by slow rotational drift of the ring orientation; the particle-level picture provides the ground truth against which spectral diagnostics are calibrated. We find that the static BBS template is preserved at every time, with the dynamics entering as a redistribution of spectral mass within that template, sharp enough to flag ring formation. We propose self-averaging of the bulk density as the mechanism behind this preservation, verified by an i.i.d.-resample comparison, and extract a small set of spectral diagnostics of the structural change computable from the distance matrix alone. We suggest that our diagnostics can be applied in other similar inverse-problem settings: financial correlation matrices, graph and network adjacency spectra, similarity matrices in molecular dynamics, and dynamics on parameter manifolds.

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