Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks,
and Quantile Regression Models provide estimates of prediction uncertainties for data …

Standard deep learning models for classification and regression applications are ideal for
capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate …

With the advent of increased computational resources and improved algorithms, machine
learning-based models are being increasingly applied to complex problems in particle …

Radio-Frequency Quadrupoles (RFQs) are multi-purpose linear particle accelerators that
simultaneously bunch and accelerate charged particle beams. The accurate simulation and …

Machine learning has emerged as a powerful solution to the modern challenges in
accelerator physics. However, the limited availability of beam time, the computational cost of …

Machine learning has emerged as a powerful solution to the modern challenges in
accelerator physics. However, the limited availability of beam time, the computational cost of …

Accelerator engineering could benefit from faster and higher-quality physics simulations.
Machine learning has emerged as a promising tool for developing accelerator simulation …

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and
outputs of large complex systems directly from data. However, for time-varying systems, the …

Accurate uncertainty estimations are essential for producing reliable machine learning
models, especially in safety-critical applications such as accelerator systems. Gaussian …

Virtual diagnostic (VD) is a computational tool based on deep learning that can be used to
predict a diagnostic output. VDs are especially useful in systems where measuring the …