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Sampling string vacua using generative models
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DOI：10.1088/2632-2153/ae32dc.png)
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Abstract
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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.
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