CYJAX: A package for Calabi-Yau metrics with JAX
Mathis Gerdes, Sven Krippendorf
Abstract
Abstract We present the first version of CYJAX, a package for machine learning Calabi–Yau metrics using JAX. It is meant to be accessible both as a top-level tool and as a library of modular functions. CYJAX is currently centered around the algebraic ansatz for the Kähler potential which automatically satisfies Kählerity and compatibility on patch overlaps. As of now, this implementation is limited to varieties defined by a single defining equation on one complex projective space. We comment on some planned generalizations. More documentation can be found at: https://cyjax.readthedocs.io . The code is available at: https://github.com/ml4physics/cyjax .
Topics & Concepts
Calabi–Yau manifoldAnsatzModular designCompatibility (geochemistry)DocumentationComputer scienceAlgebraic numberAlgebra over a fieldProgramming languageCode (set theory)Pure mathematicsMathematicsEngineeringMathematical analysisMathematical physicsSet (abstract data type)Chemical engineeringGeometry and complex manifoldsBlack Holes and Theoretical PhysicsAdvanced Algebra and Geometry