دورية أكاديمية
Bayesian data analysis for Gaussian process tomography
| العنوان: | Bayesian data analysis for Gaussian process tomography |
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| المؤلفون: | Wang, TianBo, Mazon, D., Svensson, J., Liu, A., Zhou, C., Xu, L., Hu, L., Duan, Y., Verdoolaege, Geert |
| المصدر: | JOURNAL OF FUSION ENERGY ; ISSN: 0164-0313 |
| سنة النشر: | 2019 |
| المجموعة: | Ghent University Academic Bibliography |
| مصطلحات موضوعية: | Physics and Astronomy, Bayesian inference, Data analysis, Plasma physics, Tomography, Soft X-ray, Gaussian process, Nuclear fusion, Tokamak |
| الوصف: | Bayesian inference is used in many scientific areas as a conceptually well-founded data analysis framework. In this paper, we give a brief introduction to Bayesian probability theory and its application to the tomography problem in fusion research by means of a Gaussian process prior. This Gaussian process tomography (GPT) method is used for reconstruction of the local soft X-ray (SXR) emissivity in WEST and EAST based on line-integrated data. By modeling the SXR emissivity field in a poloidal cross-section as a Gaussian process, Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time feedback information on impurity transport and for fast MHD control. In addition, the Bayesian formulism allows for uncertainty analysis of the inferred emissivity. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://biblio.ugent.be/publication/8602850Test; http://hdl.handle.net/1854/LU-8602850Test; http://dx.doi.org/10.1007/s10894-018-0205-yTest; https://biblio.ugent.be/publication/8602850/file/8602869Test |
| DOI: | 10.1007/s10894-018-0205-y |
| الإتاحة: | https://doi.org/10.1007/s10894-018-0205-yTest https://biblio.ugent.be/publication/8602850Test http://hdl.handle.net/1854/LU-8602850Test https://biblio.ugent.be/publication/8602850/file/8602869Test |
| حقوق: | No license (in copyright) ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.6CBD902C |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s10894-018-0205-y |
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