Extended spectral breakdown theory and catastrophe theory.

This chapter introduces Spectral Breakdown Theory as a novel extension of classical catastrophe theory, offering a simplified framework for analyzing singularities. Unlike René Thom’s approach, which operates in the normal space of degenerate critical points with complex and multiple roots, spectral breakdown transforms these into simple, real roots within a spectral space. This transformation is governed by two operators: R, which reduces root multiplicities, and D, which projects complex roots onto their real parts. The resulting singularities—termed spectral-derived catastrophes—are classified into three types based on the global impact t: simple (t > 0), complex (t < 0), and stable (t = 0). Each type reflects a distinct balance between the operators R and D, influencing the preservation or loss of structural information from the original catastrophe. The chapter formalizes these concepts through rigorous definitions, metrics, and examples, demonstrating how spectral singularities differ topologically and dynamically from their normal counterparts. Notably, complex and stable singularities exhibit non-proximity to normal catastrophes due to the loss of multiplicities and imaginary components, while simple singularities retain root locations but lack degeneracies. The theory is further extended into spectral geometry, which organizes singularities by their root configurations and stratifies the spectral space accordingly. Applications span complex analysis, quantum decoherence, dynamical systems, and numerical simulation. The framework offers a new lens for interpreting bifurcation phenomena and stabilizing instabilities. Future directions include formalizing the geometry of the spectral space and exploring topological invariants associated with spectral singularities.

Authors

Publication Details

Published
2025-09-25
DOI
https://doi.org/10.33774/coe-2025-drhbn
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Extended spectral breakdown theory and catastrophe theory.

Ndoubahidi Ebenezer Mbaïassem
Complex Systems and Time Series Analysis
article

Extended spectral breakdown theory and catastrophe theory.

Ndoubahidi Ebenezer Mbaïassem
article en

Abstract

This chapter introduces Spectral Breakdown Theory as a novel extension of classical catastrophe theory, offering a simplified framework for analyzing singularities. Unlike René Thom’s approach, which operates in the normal space of degenerate critical points with complex and multiple roots, spectral breakdown transforms these into simple, real roots within a spectral space. This transformation is governed by two operators: R, which reduces root multiplicities, and D, which projects complex roots onto their real parts. The resulting singularities—termed spectral-derived catastrophes—are classified into three types based on the global impact t: simple (t > 0), complex (t < 0), and stable (t = 0). Each type reflects a distinct balance between the operators R and D, influencing the preservation or loss of structural information from the original catastrophe. The chapter formalizes these concepts through rigorous definitions, metrics, and examples, demonstrating how spectral singularities differ topologically and dynamically from their normal counterparts. Notably, complex and stable singularities exhibit non-proximity to normal catastrophes due to the loss of multiplicities and imaginary components, while simple singularities retain root locations but lack degeneracies. The theory is further extended into spectral geometry, which organizes singularities by their root configurations and stratifies the spectral space accordingly. Applications span complex analysis, quantum decoherence, dynamical systems, and numerical simulation. The framework offers a new lens for interpreting bifurcation phenomena and stabilizing instabilities. Future directions include formalizing the geometry of the spectral space and exploring topological invariants associated with spectral singularities.

Openalex Percentile: Top 65%
Complex Systems and Time Series Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.