Uncertainty quantification for deep learning in particle accelerator applications
With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. Fo...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
American Physical Society
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ad0580f82c8f4e67999e607a714d29f5 |
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