Abstract
Topological Vortex Theory (TVT) is an interdisciplinary theoretical framework based on a spacetime dynamic network model. It aims to simulate and understand the dynamic behaviors of complex systems through the topological evolutionary characteristics of vortices. This theory not only provides a mechanism for information processing and learning in artificial intelligence (AI) that more closely resembles biological neural networks, but also offers novel modeling perspectives for fundamental physics issues such as quantum gravity and the nature of spacetime. This paper systematically elaborates the core connotations of TVT, including spacetime dynamic network modeling, dynamic memory mechanisms, nonlinear thinking simulation, multimodal information integration, self-organizing learning, quantized decision-making, temporal-causal extension, paths to quantized gravity, and its pursuit of unification. Furthermore, it explores the application potential and theoretical significance of TVT in both AI and fundamental physics research.
Keywords: Topological Vortex Theory; Spacetime Dynamic Network; Artificial Intelligence; Memory Model; Quantum Gravity; Interdisciplinary Modeling
1.Introduction
As research into complex systems deepens, traditional modeling methods face limitations in describing highly nonlinear, dynamically adaptive, and multimodal interactive systems. Inspired by biological neural networks and physical spacetime structures, Topological Vortex Theory (TVT) proposes a dynamic network model based on the evolution of spacetime vortices. This model simulates processes such as information transmission, memory storage, and learning adaptation through changes in topological structure, thereby not only advancing the development of brain-like artificial intelligence but also providing new mathematical tools for understanding the fundamental interactions between spacetime and matter.
The foundation of TVT is built upon quantized topological excitations. As a fundamental topological defect, the morphology and dynamics of a vortex can be precisely described by topological invariants such as winding numbers[1]. This core concept has been extended from early classical fluid vortices[2] to the realm of quantum fluids[3], providing a solid physical basis for the theory. Furthermore, modern research has integrated knot theory with field theory[4], offering crucial mathematical tools for understanding the complex structures of spacetime vortex networks, such as the interlocking of vortex chains and vortex rings[5]. On a methodological level, helicity is recognized as a key physical quantity for characterizing the three-dimensional topological structure of vortices. Its conservation properties and numerical simulation methods provide an effective approach for studying the spacetime evolution of vortex fields[6].
This paper aims to systematically elaborate the theoretical connotation of TVT and analyze its cross-application value in AI and physics.
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