论文标题
径向基函数和改进的高参数优化，以进行高斯工艺应变估计
Radial Basis Functions and Improved Hyperparameter Optimisation for Gaussian Process Strain Estimation
论文作者
论文摘要
在过去的十年中，已经发布了许多来自中子和X射线测量值的全场弹性应变估计的算法。许多最近发表的算法都依赖于将未知的应变场建模为高斯工艺（GP） - 一种概率的机器学习技术。迄今为止，基于GP的算法在未知应变场中假定了高度的平滑度和连续性。在本文中，我们对GP方法提出了三种修改，以提高性能，主要是在情况下（例如，对于高梯度或不连续的领域）；使用K折的交叉验证，径向基函数近似方案以及基于梯度的这些函数的放置的高参数优化。
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.
