cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature
This work addresses the computational challenges in string theory for physicists and mathematicians, but it appears incremental as it builds on existing numerical methods with a machine learning component.
The authors tackled the problem of numerically investigating the geometry of Calabi-Yau manifolds in string theory by introducing cymyc, a high-performance Python library that uses a geometric ansatz and machine learning to model tensor fields, resulting in a tool for approximating solutions to partial differential equations on these spaces.
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.