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Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production
<!--HTML-->We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter val...
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| Lenguaje: | eng |
| Publicado: |
2019
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| Acceso en línea: | http://cds.cern.ch/record/2672623 |