Sparse regression for plasma physics

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 identif...

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Bibliographic Details
Published in:Physics of plasmas 2023-03, Vol.30 (3)
Main Authors: Kaptanoglu, Alan A., Hansen, Christopher, Lore, Jeremy D., Landreman, Matt, Brunton, Steven L.
Format: Article
Language:English
Subjects:
Gaussian process
Image processing
Optimization
Parameter estimation
Parameter identification
Plasma physics
Regression
System identification
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Summary: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.
ISSN:1070-664X
1089-7674
DOI:10.1063/5.0139039