Dynamic Topology Geometry: Adaptive Computation for Enhanced Simulation and Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Dynamic Topology Geometry (DTG) presents a novel computational framework that leverages the intersection of topology, quantum computing, and machine learning to dynamically adjust geometric shapes and topology during numerical simulations and optimization. This approach aims to overcome the limitations of traditional methods by offering enhanced flexibility, reduced computational cost, and the potential for discovering novel geometric structures. The core of DTG lies in a novel adaptive topology transformation algorithm, coupled with a machine learning model trained to optimize geometric parameters within a quantum computing environment. This architecture promises significant improvements in simulation efficiency and the exploration of complex geometric landscapes, ultimately facilitating more effective numerical modeling and optimization tasks. This paper details the fundamental principles of the DTG framework, highlights the key innovations, and presents preliminary results demonstrating its efficacy in specific geometric transformations.

DOI: 10.5281/zenodo.22319012

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