Event generation and statistical sampling for physics with deep generative models and a density information buffer
Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-22616-z |