ChatGPT for Computational Topology

• URL: http://arxiv.org/abs/2310.07570v3
• Date: Wed, 15 Nov 2023 04:22:51 GMT
• Title: ChatGPT for Computational Topology
• Authors: Jian Liu, Li Shen and Guo-Wei Wei
• Abstract summary: ChatGPT represents a significant milestone in the field of artificial intelligence. This work endeavors to bridge the gap between theoretical topological concepts and their practical implementation in computational topology.
• Score: 10.770019251470583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: ChatGPT represents a significant milestone in the field of artificial intelligence (AI), finding widespread applications across diverse domains. However, its effectiveness in mathematical contexts has been somewhat constrained by its susceptibility to conceptual errors. Concurrently, topological data analysis (TDA), a relatively new discipline, has garnered substantial interest in recent years. Nonetheless, the advancement of TDA is impeded by the limited understanding of computational algorithms and coding proficiency among theoreticians. This work endeavors to bridge the gap between theoretical topological concepts and their practical implementation in computational topology through the utilization of ChatGPT. We showcase how a pure theoretician, devoid of computational experience and coding skills, can effectively transform mathematical formulations and concepts into functional code for computational topology with the assistance of ChatGPT. Our strategy outlines a productive process wherein a mathematician trains ChatGPT on pure mathematical concepts, steers ChatGPT towards generating computational topology code, and subsequently validates the generated code using established examples. Our specific case studies encompass the computation of Betti numbers, Laplacian matrices, and Dirac matrices for simplicial complexes, as well as the persistence of various homologies and Laplacians. Furthermore, we explore the application of ChatGPT in computing recently developed topological theories for hypergraphs and digraphs. This work serves as an initial step towards effectively transforming pure mathematical theories into practical computational tools, with the ultimate goal of enabling real applications across diverse fields.

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