Distance Preserving Machine Learning for Uncertainty Aware Accelerator
  Capacitance Predictions
Steven Goldenberg Malachi Schram Kishansingh Rajput Thomas Britton Chris Pappas Dan Lu Jared Walden Majdi I. Radaideh Sarah Cousineau Sudarshan Harave
Providing accurate uncertainty estimations is essential for producing
reliable machine learning models, especially in safety-critical applications
such as accelerator systems. Gaussian process models are generally regarded as
the gold standard method for this task, but they can struggle with large,
high-dimensional datasets. Combining deep neural networks with Gaussian process
approximation techniques have shown promising results, but dimensionality
reduction through standard deep neural network layers is not guaranteed to
maintain the distance information necessary for Gaussian process models. We
build on previous work by comparing the use of the singular value decomposition
against a spectral-normalized dense layer as a feature extractor for a deep
neural Gaussian process approximation model and apply it to a capacitance
prediction problem for the High Voltage Converter Modulators in the Oak Ridge
Spallation Neutron Source. Our model shows improved distance preservation and
predicts in-distribution capacitance values with less than 1% error.