  Over the past decade, a number of algorithms for full-field elastic strain
estimation from neutron and X-ray measurements have been published. Many of the
recently published algorithms rely on modelling the unknown strain field as a
Gaussian Process (GP) - a probabilistic machine-learning technique. Thus far,
GP-based algorithms have assumed a high degree of smoothness and continuity in
the unknown strain field. In this paper, we propose three modifications to the
GP approach to improve performance, primarily when this is not the case (e.g.
for high-gradient or discontinuous fields); hyperparameter optimisation using
k-fold cross-validation, a radial basis function approximation scheme, and
gradient-based placement of these functions.
