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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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
American Physical Society
2021-11-01
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| coleção: | Physical Review Accelerators and Beams |
| Acesso em linha: | http://doi.org/10.1103/PhysRevAccelBeams.24.114601 |
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