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@unpublished{convery:ibic2021-wepp17,
author = {O.R. Convery and Y. Gal and A. Hanuka and L. Smith},
title = {{Uncertainty Quantification and Robustness of Particle Accelerator Machine-Learning Based Models}},
booktitle = {Proc. IBIC'21},
language = {english},
intype = {presented at the},
series = {International Beam Instrumentation Conference},
number = {10},
venue = {Pohang, Rep. of Korea},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2021},
note = {presented at IBIC'21 in Pohang, Rep. of Korea, unpublished},
abstract = {{Virtual Diagnostic (VD) is a computational tool built using deep learning that can be used to predict diagnostic outputs*. VDs are especially useful in systems where measuring outputs is invasive, limited, costly or runs the risk of modifying the output. In experiments with large ramifications, it is important to quantify the uncertainty of each prediction. Given out-of-distribution inputs (e.g. using the same machine in a different operation mode), it is also necessary to understand how robust the VD model is and how well it generalizes on unfamiliar data. In this work**, we use various compositions of neural networks to explore and enhance prediction uncertainty and robustness on data sets gathered from SLAC National Laboratory. We aim to accurately and confidently predict the longitudinal phase space images of electron beams. The ability to make informed decisions under uncertainty and limited computational power is crucial for reliable deployment of scalable deep learning tools on safety-critical systems such as particle accelerators.}},
}