We measure the CO-to-H2conversion factor (
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi–Yau manifolds using deep learning. In this paper, we consider the dataset of all Calabi–Yau four-folds constructed as complete intersections in products of projective spaces. Employing neural networks inspired by state-of-the-art computer vision architectures, we improve earlier benchmarks and demonstrate that all four non-trivial Hodge numbers can be learned at the same time using a multi-task architecture. With 30% (80%) training ratio, we reach an accuracy of 100% for
- Award ID(s):
- 2019786
- NSF-PAR ID:
- 10303335
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- Article No. 015006
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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