用机器学习求解卡拉比-丘流形的几何张量场，加速弦论研究。
cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature
- 基于几何假设构建张量场数值模型，支持任意阶张量。
- 通过求解偏微分方程系统，高效逼近物理相关张量场。
- 适合弦论、高维几何与机器学习交叉研究者使用。
我们提出 exttt{cymyc}，一个高性能的 Python 库，用于数值研究一大类弦论紧致化流形及其模空间的几何性质。该库建立了一个明确的几何假设，可对一大类卡拉比-丘流形上的任意阶张量场进行数值建模。 exttt{cymyc} 包含一个机器学习模块，利用该假设，通过寻找张量场应满足的偏微分方程系统的近似解，来建模这些空间中感兴趣的张量场。
原文摘要 · Abstract (English)
We introduce \texttt{cymyc}, a high-performance Python library for numerical investigation of the geometry of a large class of string compactification manifolds and their associated moduli spaces. We develop a well-defined geometric ansatz to numerically model tensor fields of arbitrary degree on a large class of Calabi-Yau manifolds. \texttt{cymyc} includes a machine learning component which incorporates this ansatz to model tensor fields of interest on these spaces by finding an approximate solution to the system of partial differential equations they should satisfy.
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