Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression
Abstract
The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.
- Authors:
- Publication Date:
- DOE Contract Number:
- SC0014264
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- Subject:
- 70 PLASMA PHYSICS AND FUSION TECHNOLOGY
- OSTI Identifier:
- 1882413
- DOI:
- https://doi.org/10.7910/DVN/YNGFYK
Citation Formats
Mathews, Abhilash, and Hughes, Jerry W. Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression. United States: N. p., 2021.
Web. doi:10.7910/DVN/YNGFYK.
Mathews, Abhilash, & Hughes, Jerry W. Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression. United States. doi:https://doi.org/10.7910/DVN/YNGFYK
Mathews, Abhilash, and Hughes, Jerry W. 2021.
"Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression". United States. doi:https://doi.org/10.7910/DVN/YNGFYK. https://www.osti.gov/servlets/purl/1882413. Pub date:Thu May 06 00:00:00 EDT 2021
@article{osti_1882413,
title = {Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression},
author = {Mathews, Abhilash and Hughes, Jerry W.},
abstractNote = {The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.},
doi = {10.7910/DVN/YNGFYK},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {5}
}
Works referencing / citing this record:
Quantifying Experimental Edge Plasma Evolution Via Multidimensional Adaptive Gaussian Process Regression
journal, December 2021
- Mathews, Abhilash; Hughes, Jerry W.
- IEEE Transactions on Plasma Science, Vol. 49, Issue 12