构建统一的几何拓扑对称学习库，让神经网络更好理解复杂结构数据。
TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
- 融合拓扑代数几何思想，提供统一的PyTorch工具集
- 支持结构化数据预处理、模型架构与训练分析全流程
- 适合研究几何深度学习或需对称性建模的开发者
过去十年，神经网络被应用于越来越多具有丰富几何、拓扑或对称结构的数据。研究人员从中汲取拓扑、代数和几何的思想，发展出多种新方法。然而，支撑这些方法的软件生态仍分散零落，许多重要算法仅以未维护的原型代码形式存在。为此，我们提出拓扑、代数与几何张量（TAGTorch），一个基于PyTorch的开源库，整合了受拓扑、代数和几何启发的工具，涵盖数据预处理、模型架构、训练技术及模型分析工具。本文阐述了其设计哲学与当前架构，强调其在填补现有软件生态空白方面的潜力，并讨论未来开发重点。
原文摘要 · Abstract (English)
Over the last decade, neural networks have been applied to an increasingly diverse range of applications, including data with rich geometric, topological, or symmetry-related structure. As a result, researchers have increasingly drawn inspiration from topology, algebra, and geometry. Despite this rich algorithmic development, the supporting software ecosystem remains fragmented. Many important methods exist only as research prototypes in unmaintained repositories. We address this by introducing Topology, Algebra, and Geometry Torch (TAGTorch), an open-source, PyTorch-based library that unifies tools inspired by topology, algebra, and geometry, including data-preprocessing methods, architectures, training techniques, and model analysis tools. We describe the design philosophy of TAGTorch and then discuss its current architecture and capabilities, highlighting areas where it can fill gaps in the current software ecosystem. We conclude with a discussion of our future development priorities for the library.
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