Geometric Transduction Theory: a Relational Architecture for the Evolution, Memory, and Transformation of Forms
Year: 2026
Type: preprint
Abstract: GEOMETRIC TRANSDUCTION THEORY: A RELATIONAL ARCHITECTURE FOR THE EVOLUTION, MEMORY, AND TRANSFORMATION OF FORMS This article presents Geometric Transduction Theory as a relational and mathematical framework for understanding the evolution, transformation, and memory of geometric forms. Developed within the broader perspective of Relational Architecture, the theory considers geometric configurations as dynamic states whose identity encompasses both their present spatial manifestation and the history of their transformations. The central structure is expressed through the relational geometric state: [\Sigma_G=(G,M_G)] where (G) represents the current geometric configuration and (M_G) represents Relational Geometric Memory, incorporating invariants, accumulated differences, and transformation trajectories. The article organizes geometric evolution into four fundamental modes: transformational evolution, deformational evolution, intrinsic geometric flow, and metric evolution. These modes provide a unified conceptual structure for examining changes in position, orientation, scale, morphology, curvature, dimensionality, and the metric organization of space. The proposed architecture incorporates classical geometric transformations, affine and projective structures, shape theory, topology, Mean Curvature Flow, and Ricci Flow. These mathematical structures are integrated through the concept of geometric transduction, understood as the passage between configurations through transformation operators and relational parameters. A central contribution is the introduction of Relational Geometric Memory as an integral component of geometric evolution. Memory records the invariants and differences associated with a transformation, allowing the trajectory of a configuration to be considered alongside its present form. Consequently, configurations that reach equivalent final geometries may retain distinct relational histories. The article also develops the concept of dimensional transduction, examining the passage from points to trajectories, curves to surfaces, and surfaces to volumes. These processes are organized through the incorporation of new degrees of freedom and the transformation of existing relational structures. Metamorphosis, topological preservation, and changes in geometric organization are incorporated into the same theoretical architecture. Another central element is Relational Shape Space, presented as a mathematical environment for representing geometric configurations, comparing forms, and describing transformation trajectories. Through this structure, geometric evolution can be investigated as an organized sequence of relational states rather than as a collection of isolated figures. The theory establishes a General Law of Geometric Transduction, integrating initial configurations, transformation operators, evolution parameters, geometric constraints, and relational memory. Its framework provides a common conceptual basis for studying discrete transformations, continuous deformations, intrinsic flows, metric evolution, and dimensional transitions. The article concludes by establishing research directions involving computational modeling, Artificial Intelligence, spectral and topological investigation, and the analysis of geometric singularities. These applications extend the proposed architecture to natural, physical, and computational systems in which form, transformation, and memory constitute interconnected dimensions of evolution. The central proposition is that geometric form can be understood through the relationship between its present configuration, its transformation processes, and the memory of its trajectory. Geometric Transduction Theory therefore establishes an integrated framework for investigating not only what forms are, but how they evolve, how they preserve or reorganize their relational structures, and how successive configurations emerge from prior states. Keywords: Geometric Transduction Theory; Relational Architecture; Geometric Evolution; Relational Geometric Memory; Geometric Flows; Topological Invariants; Shape Space; Geometric Transformations; Dimensional Transduction; Morphogenesis; Metric Evolution; Mean Curvature Flow; Ricci Flow; Topology; Transformation Operators; Computational Geometry. Author: Cláudio Vicente da SilvaIndependent Researcher — Londrina, Paraná, BrazilPublication date: November 2026
Author: Vicente da Silva, Cláudio
Language: English
Topic: Mathematics and Applications
Subfield: Geometry and Topology
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225628042
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