  Researchers in the field of ultra-intense laser science are beginning to
embrace machine learning methods. In this study we consider three different
machine learning methods -- a two-hidden layer neural network, Support Vector
Regression and Gaussian Process Regression -- and compare how well they can
learn from a synthetic data set for proton acceleration in the Target Normal
Sheath Acceleration regime. The synthetic data set was generated from a
previously published theoretical model by Fuchs et al. 2005 that we modified.
Once trained, these machine learning methods can assist with efforts to
maximize the peak proton energy, or with the more general problem of
configuring the laser system to produce a proton energy spectrum with desired
characteristics. In our study we focus on both the accuracy of the machine
learning methods and the performance on one GPU including the memory
consumption. Although it is arguably the least sophisticated machine learning
model we considered, Support Vector Regression performed very well in our
tests.
