- Title
- Radial basis functions and improved hyperparameter optimisation for gaussian process strain estimation
- Creator
- Gregg, A. W. T.; Hendriks, J. N.; Wensrich, C. M.; O'Dell, N.
- Relation
- ARC.DP170102324 http://purl.org/au-research/grants/arc/DP170102324
- Relation
- Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms Vol. 480, p. 67-77
- Publisher Link
- http://dx.doi.org/10.1016/j.nimb.2020.08.003
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2020
- Description
- 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 -fold cross-validation, a radial basis function approximation scheme, and gradient-based placement of these functions.
- Subject
- bragg-edge; gaussian; imaging; neutron; process; strain; tomography
- Identifier
- http://hdl.handle.net/1959.13/1444347
- Identifier
- uon:42274
- Identifier
- ISSN:0168-583X
- Rights
- © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
- Language
- eng
- Full Text
- Reviewed
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