Conditional generative models for sampling and phase transition indication in spin systems
In this work, we study generative adversarial networks (GANs) as a tool to learn the distribution of spin configurations and to generate samples, conditioned on external tuning parameters or other quantities associated with individual configurations. For concreteness, we focus on two examples of con...
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| Format: | Article |
| Language: | English |
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SciPost
2021-08-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.11.2.043 |