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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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
American Physical Society
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/ad0580f82c8f4e67999e607a714d29f5 |
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