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Application of Gaussian Process Regression to the Current-Voltage Characteristics of a Langmuir Probe

Yuichi KAWACHI, Atsushi OKAMOTO, Koki SATO, Yuto YAMADA, Kota TAKEDA, Takaaki FUJITA

Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

(Received 6 March 2025 / Accepted 6 April 2025 / Published 22 May 2025)

Abstract

We evaluate the performance of Gaussian Process Regression (GPR) in estimating the current-voltage characteristics of a Langmuir probe, as well as its first and second derivatives. The results show good agreement between the estimated and measured data. When comparing GPR with the conventional Savitzky-Golay filter, we find that GPR is comparable to the Savitzky-Golay filter in terms of the accuracy of the estimated data. The uncertainty of the estimated data is also evaluated, and the results indicate that GPR underestimates the uncertainty of the electron current. This is likely due to the assumption of a homoscedastic noise model in the standard GPR.

Copyright (c) 2025 The Japan Society of Plasma Science and Nuclear Fusion Research

Keywords

Langmuir probe, Gaussian process regression, uncertainty quantification, electron energy distribution function

DOI: 10.1585/pfr.20.1201030

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