A Gaussian process guide for signal regression in magnetic fusion Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.1088/1741-4326/ad1af5
Extracting reliable information from diagnostic data in tokamaks is critical for understanding, analyzing, and controlling the behavior of fusion plasmas and validating models describing that behavior. Recent interest within the fusion community has focused on the use of principled statistical methods, such as Gaussian process regression (GPR), to attempt to develop sharper, more reliable, and more rigorous tools for examining the complex observed behavior in these systems. While GPR is an enormously powerful tool, there is also the danger of drawing fragile, or inconsistent conclusions from naive GPR fits that are not driven by principled treatments. Here we review the fundamental concepts underlying GPR in a way that may be useful for broad-ranging applications in fusion science. We also revisit how GPR is developed for profile fitting in tokamaks. We examine various extensions and targeted modifications applicable to experimental observations in the edge of the DIII-D tokamak. Finally, we discuss best practices for applying GPR to fusion data.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1741-4326/ad1af5
- https://iopscience.iop.org/article/10.1088/1741-4326/ad1af5/pdf
- OA Status
- diamond
- Cited By
- 6
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390574425
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4390574425Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1741-4326/ad1af5Digital Object Identifier
- Title
-
A Gaussian process guide for signal regression in magnetic fusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-04Full publication date if available
- Authors
-
Craig Michoski, Todd Oliver, D. R. Hatch, A. Diallo, Mike Kotschenreuther, D. Eldon, Matthew M. Waller, R. J. Groebner, A. NelsonList of authors in order
- Landing page
-
https://doi.org/10.1088/1741-4326/ad1af5Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1741-4326/ad1af5/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1741-4326/ad1af5/pdfDirect OA link when available
- Concepts
-
Ground-penetrating radar, Kriging, Tokamak, Fusion, Process (computing), Computer science, Sensor fusion, Gaussian, Gaussian process, Regression, Artificial intelligence, Machine learning, Enhanced Data Rates for GSM Evolution, Ranging, Data mining, Pattern recognition (psychology), Plasma, Physics, Mathematics, Statistics, Radar, Nuclear physics, Philosophy, Quantum mechanics, Linguistics, Operating system, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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