Advanced search
1 file | 1.38 MB Add to list

Bayesian data analysis for Gaussian process tomography

(2019) JOURNAL OF FUSION ENERGY. 38(3-4). p.445-457
Author
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

Downloads

  • Bayesian Data analysis and Gaussian Process Tomography.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.38 MB

Citation

Please use this url to cite or link to this publication:

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://dx.doi.org/10.1007/s10894-018-0205-y},
  volume       = {38},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: