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.
Authors
- W. A. Zúñiga‐Galindo (ORCID: https://orcid.org/0000-0002-6332-2520)
Institutions
- The University of Texas Rio Grande Valley (US)
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
- 0.00