Uncertainty aware machine-learning-based surrogate models for particle accelerators: study at the fermilab booster accelerator complex

HIGHLIGHTS

• who: Malachi Schram from the Newport News, Virginia, USA University of have published the article: Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex, in the Journal: (JOURNAL) of 21/04/2023
• what: The authors compare three different methods to estimate data-related uncertainties for DNN models as it applies to modeling the FNAL booster accelerator complex. The authors develop the DNN model to simultaneously learn predictions based on a set of defined quantiles.
• how: The authors implemented a deep learning model with convolutional layers . . .
  

   

  

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