Topological Vortex Theory and Its Applications in Artificial Intelligence (5)

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Сообщение 2025-12-17 02:47:05
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5. Conclusion

As a novel modeling framework based on spacetime dynamic networks, Topological Vortex Theory not only provides more biologically plausible learning and memory mechanisms for artificial intelligence but also offers innovative perspectives for understanding spacetime, gravity, and quantum phenomena. Its interdisciplinary nature demonstrates the potential to unify the dynamics of complex systems with the laws of fundamental physics, promising to catalyze breakthrough progress at the frontiers of science and technology.

References

  1. Simula, T. (2023). Quantised vortices: A handbook of topological excitations. Harbin Institute of Technology Press.
  2. Huang, Y., & Shi, W. (1998). Topological structures of vortex and helicity analysis. Acta Mechanica Sinica, 14 (3), 208–214.
  3. Duan, Y., & Zhang, P. (2001). Vortex in Generalized Gross-Pitaevskii Theory. Nuclear Physics Review, 18 (4), 225–231.
  4. Ricca, R. L., & Liu, X. (Eds.). (2024). Knotted fields. In Lecture Notes in Mathematics (Vol. 2344). Springer International Publishing.
  5. Ricca, R. L. (2023). Geometric and topological fluid mechanics: Selected overview and new challenges. Fluid Dynamics Research, 55 (4), 041401.
  6. Shen, H. (2022). Helicity is an important physical quantity to describe the topological properties of fluid vortices. In Abstracts of the 12th National Conference on Fluid Mechanics (p. 23). Chinese Society of Theoretical and Applied Mechanics.

Academic Statement

The Topological Vortex Theory described in this paper is currently primarily in the conceptual framework and theoretical deduction stage. The completion of its mathematical formalization, the implementation of computational models, and its empirical testing in physical or AI systems require extensive subsequent interdisciplinary collaborative research for advancement and validation. The author welcomes constructive discussion and collaboration from academic peers on this matter. Neither the author nor the platform publishing this paper shall be held responsible for any direct or indirect consequences arising from derivative applications (such as technology development, investment decisions, etc.) based on the theoretical viewpoints presented herein. Theoretical exploration involves uncertainty, and application transformation requires rigorous scientific and experimental verification.

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