Uncertainty Aware Deep Learning for Particle Accelerators
Kishansingh Rajput Malachi Schram Karthik Somayaji
Standard deep learning models for classification and regression applications
are ideal for capturing complex system dynamics. However, their predictions can
be arbitrarily inaccurate when the input samples are not similar to the
training data. Implementation of distance aware uncertainty estimation can be
used to detect these scenarios and provide a level of confidence associated
with their predictions. In this paper, we present results from using Deep
Gaussian Process Approximation (DGPA) methods for errant beam prediction at
Spallation Neutron Source (SNS) accelerator (classification) and we provide an
uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL)
Booster Accelerator Complex (regression).