The Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data

This repository contains a self-contained presentation of a general construction: how to turn any bounded discrete sequence (values in [−1, 1], placed at scattered points in ℝ³) into a smooth field suitable as the coefficient of a uniformly elliptic differential operator. The construction uses a normalized mollification (a Nadaraya–Watson quotient). The normalization is essential: the unnormalized sum is not bounded by 1, since the mollifier peak grows like ε⁻³. The denominator restores the bound |t(x)| ≤ 1 for every choice of scale and placement — the single property on which all of the elliptic analysis depends. From this bound the paper derives uniform ellipticity, a pointwise flux bound, two-sided energy coercivity, and a Lyapunov dissipation estimate. These results use only the boundedness of the coefficient and standard elliptic theory; they hold for any bounded sequence. As a concrete worked example, the construction is applied to the Leue Modulation Coefficients (LMC) — the normalized elliptic-curve trace values tₚ = aₚ/(2√p), which satisfy |tₚ| ≤ 1 by the Hasse bound. The example illustrates the method but does not restrict it; the arithmetic origin of the coefficients is not used in any proof. This Zenodo package includes: the full research paper (PDF), a Python implementation generating the field and conductivity model, a numerical plot comparing the discrete values with the smoothed field, a cover letter and supporting documentation. The contribution is methodological: a clean, reusable recipe for embedding bounded discrete data as controlled PDE coefficients, with the arithmetic example serving as illustration rather than as the source of the analytic content.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-14
DOI
https://doi.org/10.5281/zenodo.17760724
Primary Topic
Numerical methods in inverse problems
Type
preprint
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preprint

The Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data

Zenodo (CERN European Organization for Nuclear Research)
Numerical methods in inverse problems
preprint

The Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data

preprint en

Abstract

This repository contains a self-contained presentation of a general construction: how to turn any bounded discrete sequence (values in [−1, 1], placed at scattered points in ℝ³) into a smooth field suitable as the coefficient of a uniformly elliptic differential operator. The construction uses a normalized mollification (a Nadaraya–Watson quotient). The normalization is essential: the unnormalized sum is not bounded by 1, since the mollifier peak grows like ε⁻³. The denominator restores the bound |t(x)| ≤ 1 for every choice of scale and placement — the single property on which all of the elliptic analysis depends. From this bound the paper derives uniform ellipticity, a pointwise flux bound, two-sided energy coercivity, and a Lyapunov dissipation estimate. These results use only the boundedness of the coefficient and standard elliptic theory; they hold for any bounded sequence. As a concrete worked example, the construction is applied to the Leue Modulation Coefficients (LMC) — the normalized elliptic-curve trace values tₚ = aₚ/(2√p), which satisfy |tₚ| ≤ 1 by the Hasse bound. The example illustrates the method but does not restrict it; the arithmetic origin of the coefficients is not used in any proof. This Zenodo package includes: the full research paper (PDF), a Python implementation generating the field and conductivity model, a numerical plot comparing the discrete values with the smoothed field, a cover letter and supporting documentation. The contribution is methodological: a clean, reusable recipe for embedding bounded discrete data as controlled PDE coefficients, with the arithmetic example serving as illustration rather than as the source of the analytic content.

Zenodo (CERN European Organization for Nuclear Research)
Numerical methods in inverse problems
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