Bayesian data analysis for Gaussian process tomography
- Author
- TianBo Wang, D. Mazon, J. Svensson, A. Liu, C. Zhou, L. Xu, L. Hu, Y. Duan and Geert Verdoolaege (UGent)
- Organization
- Abstract
- 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.
- Keywords
- Bayesian inference, Data analysis, Plasma physics, Tomography, Soft X-ray, Gaussian process, Nuclear fusion, Tokamak
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8602850
- MLA
- Wang, TianBo, et al. “Bayesian Data Analysis for Gaussian Process Tomography.” JOURNAL OF FUSION ENERGY, vol. 38, no. 3–4, 2019, pp. 445–57, doi:10.1007/s10894-018-0205-y.
- APA
- Wang, T., Mazon, D., Svensson, J., Liu, A., Zhou, C., Xu, L., … Verdoolaege, G. (2019). Bayesian data analysis for Gaussian process tomography. JOURNAL OF FUSION ENERGY, 38(3–4), 445–457. https://doi.org/10.1007/s10894-018-0205-y
- Chicago author-date
- Wang, TianBo, D. Mazon, J. Svensson, A. Liu, C. Zhou, L. Xu, L. Hu, Y. Duan, and Geert Verdoolaege. 2019. “Bayesian Data Analysis for Gaussian Process Tomography.” JOURNAL OF FUSION ENERGY 38 (3–4): 445–57. https://doi.org/10.1007/s10894-018-0205-y.
- Chicago author-date (all authors)
- Wang, TianBo, D. Mazon, J. Svensson, A. Liu, C. Zhou, L. Xu, L. Hu, Y. Duan, and Geert Verdoolaege. 2019. “Bayesian Data Analysis for Gaussian Process Tomography.” JOURNAL OF FUSION ENERGY 38 (3–4): 445–457. doi:10.1007/s10894-018-0205-y.
- Vancouver
- 1.Wang T, Mazon D, Svensson J, Liu A, Zhou C, Xu L, et al. Bayesian data analysis for Gaussian process tomography. JOURNAL OF FUSION ENERGY. 2019;38(3–4):445–57.
- IEEE
- [1]T. Wang et al., “Bayesian data analysis for Gaussian process tomography,” JOURNAL OF FUSION ENERGY, vol. 38, no. 3–4, pp. 445–457, 2019.
@article{8602850,
abstract = {{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.}},
author = {{Wang, TianBo and Mazon, D. and Svensson, J. and Liu, A. and Zhou, C. and Xu, L. and Hu, L. and Duan, Y. and Verdoolaege, Geert}},
issn = {{0164-0313}},
journal = {{JOURNAL OF FUSION ENERGY}},
keywords = {{Bayesian inference,Data analysis,Plasma physics,Tomography,Soft X-ray,Gaussian process,Nuclear fusion,Tokamak}},
language = {{eng}},
number = {{3-4}},
pages = {{445--457}},
title = {{Bayesian data analysis for Gaussian process tomography}},
url = {{http://doi.org/10.1007/s10894-018-0205-y}},
volume = {{38}},
year = {{2019}},
}
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