Reproducibility archive for "Reconstructing hidden networks from snap-through and switching thresholds: a self-calibrating inverse method"

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22821580
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Reproducibility archive for "Reconstructing hidden networks from snap-through and switching thresholds: a self-calibrating inverse method"

Marcos André Simonssini, LUCAS RIBEIRO
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Reproducibility archive for "Reconstructing hidden networks from snap-through and switching thresholds: a self-calibrating inverse method"

Marcos André Simonssini, LUCAS RIBEIRO
article en

Abstract

Fixed-seed reproducibility archive for the article "Reconstructing hidden networks from snap-through and switching thresholds: a self-calibrating inverse method" (submitted to The European Physical Journal Plus). The article develops an inverse method that reconstructs a hidden symmetric weighted network from the local geometry of saddle-node thresholds — snap-through loads in multistable mechanical networks, switching thresholds in nonlinear resistive circuits — without transient time series or direct access to the internal couplings. For a simple homogeneous fold, the normalized threshold Hessian satisfies K(gamma) = L^+ + gamma (L^+)^2, where L^+ is the Moore-Penrose pseudoinverse of the hidden weighted Laplacian. This archive contains the single Python script that regenerates every numerical result reported in the article, together with the reference outputs it produces: exact algebraic stress tests, cubic-remainder scaling, end-to-end edge reconstruction, noise and contrast-design experiments, actuator-gain and unknown-contrast self-calibration, finite-amplitude estimators, spectral-diversity identifiability certificates, and the near-degenerate conditioning study. All experiments are synthetic; no laboratory tomography data are included or claimed. Contents: reproduce_numerics.py, requirements.txt, README.txt, and reference_outputs/ with 30 machine-readable files (CSV, JSON, PNG). Usage: python reproduce_numerics.py All random seeds are fixed, so the script reproduces the reference outputs exactly. All derivations, assumptions, parameter ranges, and reported values needed to assess the article's claims are contained in the article itself; this archive is provided for exact computational reproduction only.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 10%
Model Reduction and Neural Networks
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