| Autor: |
Mathis Gerdes, Sven Krippendorf |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Machine Learning: Science and Technology, Vol 4, Iss 2, p 025031 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2632-2153 |
| DOI: |
10.1088/2632-2153/acdc84 |
| Popis: |
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 . |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
