Bayesian Data Analysis for Gaussian Process Tomography

T. Wang; D. Mazon; J. Svensson; A. Liu; C. Zhou; L. Xu; L. Hu; Y. Duan; G. Verdoolaege

doi:10.1007/s10894-018-0205-y

Bayesian Data Analysis for Gaussian Process Tomography

T. Wang, D. Mazon, J. Svensson, A. Liu, C. Zhou, L. Xu, L. Hu, Y. Duan, G. Verdoolaege

Laboratory for Plasma Physics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	445-457
Number of pages	13
Journal	Journal of Fusion Energy
Volume	38
Issue number	3-4
DOIs	https://doi.org/10.1007/s10894-018-0205-y
Publication status	Published - 1 Aug 2019

Keywords

Bayesian inference
Data analysis
Gaussian process
Nuclear fusion
Plasma physics
Soft X-ray
Tokamak
Tomography

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10.1007/s10894-018-0205-y

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title = "Bayesian Data Analysis for Gaussian Process Tomography",

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.",

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author = "T. Wang and D. Mazon and J. Svensson and A. Liu and C. Zhou and L. Xu and L. Hu and Y. Duan and G. Verdoolaege",

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AU - Wang, T.

AU - Mazon, D.

AU - Svensson, J.

AU - Liu, A.

AU - Zhou, C.

AU - Xu, L.

AU - Hu, L.

AU - Duan, Y.

AU - Verdoolaege, G.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Data analysis

KW - Gaussian process

KW - Nuclear fusion

KW - Plasma physics

KW - Soft X-ray

KW - Tokamak

KW - Tomography

UR - https://www.scopus.com/pages/publications/85057003838

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VL - 38

SP - 445

EP - 457

JO - Journal of Fusion Energy

JF - Journal of Fusion Energy

IS - 3-4

ER -

Bayesian Data Analysis for Gaussian Process Tomography

Abstract

Keywords

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