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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.16029 |
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| _version_ | 1866915721823387648 |
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| author | Walden, Moritz Larfors, Magdalena |
| author_facet | Walden, Moritz Larfors, Magdalena |
| contents | We apply generative models to a key problem in the string compactification program, namely construction of type IIB string vacua. To this end, we make use of a Bayesian Flow Network, a generative model capable of handling discrete data, to generate flux vectors that give rise to type IIB vacua. Furthermore, we sample flux vacua that have certain desirable properties by employing a Transformer as a conditional generative model. Both models demonstrate good performance in finding flux vacua and thus prove to be powerful tools in the exploration of the string landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sampling String Vacua Using Generative Models Walden, Moritz Larfors, Magdalena High Energy Physics - Theory We apply generative models to a key problem in the string compactification program, namely construction of type IIB string vacua. To this end, we make use of a Bayesian Flow Network, a generative model capable of handling discrete data, to generate flux vectors that give rise to type IIB vacua. Furthermore, we sample flux vacua that have certain desirable properties by employing a Transformer as a conditional generative model. Both models demonstrate good performance in finding flux vacua and thus prove to be powerful tools in the exploration of the string landscape. |
| title | Sampling String Vacua Using Generative Models |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2509.16029 |