Supplementary Material . Noise-Robust Finite-Time Regime Separation in Linear Network Dynamics

This supplementary document provides additional experimental results, analytical details, and implementation information supporting the main manuscript. All simulations are based on synthetic network models and linear update rules. The main article is fully self-contained; this material is provided for transparency and reproducibility.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-29
DOI
https://doi.org/10.5281/zenodo.18419860
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Supplementary Material . Noise-Robust Finite-Time Regime Separation in Linear Network Dynamics

Haluk Aksak
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Supplementary Material . Noise-Robust Finite-Time Regime Separation in Linear Network Dynamics

Haluk Aksak
article en

Abstract

This supplementary document provides additional experimental results, analytical details, and implementation information supporting the main manuscript. All simulations are based on synthetic network models and linear update rules. The main article is fully self-contained; this material is provided for transparency and reproducibility.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 72%
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Supplementary Material . Noise-Robust Finite-Time Regime Separation in Linear Network Dynamics — Haluk Aksak · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS