Many scientific problems can be formulated as sparse regression, i.e., regression onto a set of parameters when there is a desire or expectation that some of the parameters are exactly zero or do not substantially contribute. This includes many problems in signal and image processing, system identification, optimization, and parameter estimation methods such as Gaussian process regression. Sparsity facilitates exploring high-dimensional spaces while finding parsimonious and interpretable solutions. In the present work, we illustrate some of the important ways in which sparse regression appears in plasma physics and point out recent contributions and remaining challenges to solving these problems in this field. A brief review is provided for the optimization problem and the state-of-the-art solvers, especially for constrained and high-dimensional sparse regression.
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Sparse regression for plasma physics
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March 2023
Research Article|
March 27 2023
Sparse regression for plasma physics
Special Collection:
Papers from the 64th Annual Meeting of the APS Division of Plasma Physics
Alan A. Kaptanoglu
;
1
Institute for Research in Electronics and Applied Physics, University of Maryland
, College Park, Maryland 20742, USA
2
Department of Mechanical Engineering, University of Washington
, Seattle, Washington 98195, USA
b)Author to whom correspondence should be addressed: akaptano@umd.edu
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Christopher Hansen
;
Christopher Hansen
(Conceptualization, Supervision, Writing – review & editing)
3
Department of Aeronautics and Astronautics, University of Washington
, Seattle, Washington 98195, USA
4
Department of Applied Physics and Applied Mathematics, Columbia University
, New York, New York, 10027, USA
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Jeremy D. Lore
;
Jeremy D. Lore
(Investigation, Methodology)
5
Oak Ridge National Laboratory
, Oak Ridge, Tennessee 37831, USA
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Matt Landreman
;
Matt Landreman
(Conceptualization, Software, Supervision)
1
Institute for Research in Electronics and Applied Physics, University of Maryland
, College Park, Maryland 20742, USA
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Steven L. Brunton
Steven L. Brunton
(Conceptualization, Supervision, Writing – review & editing)
2
Department of Mechanical Engineering, University of Washington
, Seattle, Washington 98195, USA
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a)
Invited speaker.
b)Author to whom correspondence should be addressed: akaptano@umd.edu
Note: This paper is part of the Special Collection: Papers from the 64th Annual Meeting of the APS Division of Plasma Physics.
Note: Paper VI2 1, Bull. Am. Phys. Soc. 67 (2022).
Phys. Plasmas 30, 033906 (2023)
Article history
Received:
December 16 2022
Accepted:
February 26 2023
Citation
Alan A. Kaptanoglu, Christopher Hansen, Jeremy D. Lore, Matt Landreman, Steven L. Brunton; Sparse regression for plasma physics. Phys. Plasmas 1 March 2023; 30 (3): 033906. https://doi.org/10.1063/5.0139039
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