Deep Neural Networks: A Formulation via Non-Archimedean Analysis

Abstract We introduce a new class of deep neural networks (DNNs) with multilayered tree-like architectures. The architectures are codified by using numbers in the ring of integers of non-Archimedean local fields. These rings have a natural hierarchical organization as infinite rooted trees. Natural morphisms on these rings allow us to construct finite multilayered architectures. The new DNNs are robust universal approximators of real-valued functions defined on the mentioned rings. We also show that the DNNs are robust universal approximators of real-valued square-integrable functions defined in the unit interval.

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Publication Details

Journal
Journal of Fourier Analysis and Applications
Published
2026-07-27
DOI
https://doi.org/10.1007/s00041-026-10290-y
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
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article

Deep Neural Networks: A Formulation via Non-Archimedean Analysis

W. A. Zúñiga‐Galindo
Journal of Fourier Analysis and Applications
Computational Physics and Python Applications
article

Deep Neural Networks: A Formulation via Non-Archimedean Analysis

W. A. Zúñiga‐Galindo
article en

Abstract

Abstract We introduce a new class of deep neural networks (DNNs) with multilayered tree-like architectures. The architectures are codified by using numbers in the ring of integers of non-Archimedean local fields. These rings have a natural hierarchical organization as infinite rooted trees. Natural morphisms on these rings allow us to construct finite multilayered architectures. The new DNNs are robust universal approximators of real-valued functions defined on the mentioned rings. We also show that the DNNs are robust universal approximators of real-valued square-integrable functions defined in the unit interval.

Journal of Fourier Analysis and ApplicationsVol. 32(4)
The University of Texas Rio Grande Valley (US)
Openalex Percentile: Top 100%
Computational Physics and Python Applications
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