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.

在过去的十年中，已经发布了许多用于通过中子和X射线测量进行全场弹性应变估计的算法。最近发布的许多算法都依赖于将未知应变场建模为高斯过程（GP），这是一种概率性机器学习技术。迄今为止，基于GP的算法已在未知应变场中假定了高度的平滑度和连续性。在本文中，我们提出了对GP方法的三种修改，以提高性能，主要是在情况并非如此的情况下（例如，对于高梯度场或不连续场）；使用的超参数优化$ķ$折交叉验证，径向基函数逼近方案以及这些函数的基于梯度的放置。