Plasma parameter profile inference from limited data utilizing second-order derivative priors and physic-based constraints

T. Nishizawa, M. Cavedon, R. Dux, F. Reimold, U. Von Toussaint

研究成果: ジャーナルへの寄稿学術誌査読

4 被引用数 (Scopus)

抄録

A Bayesian framework has been used to improve the quality of inferred plasma parameter profiles. An integrated data analysis allows for coherent combinations of different diagnostics, and Gaussian process regression provides a reliable regularization process and systematic uncertainty estimation. In this paper, we propose a new profile inference framework that utilizes our prior knowledge about plasma physics, along with integrated data analysis and a Gaussian process. In order to facilitate the use of the Markov chain Monte Carlo sampling, we use a Gaussian process to define quantities corresponding to the second derivatives of the profiles. We validate the analysis technique by using a synthetic one-dimensional plasma, in which the transport properties are known and demonstrate that the proposed analysis technique can infer plasma parameter profiles from line-integrated measurements only. Furthermore, we can even infer unknown parameters in our physics models when our physics knowledge on the system is incomplete. This analysis framework is applicable to laboratory plasmas and provides a means to investigate plasma parameters, to which standard diagnostics are not directly sensitive.

本文言語英語
論文番号032504
ジャーナルPhysics of Plasmas
28
3
DOI
出版ステータス出版済み - 3月 1 2021
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 凝縮系物理学

フィンガープリント

「Plasma parameter profile inference from limited data utilizing second-order derivative priors and physic-based constraints」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル