Sparse regression for plasma physics

Author:

Kaptanoglu Alan A.12ORCID,Hansen Christopher34ORCID,Lore Jeremy D.5ORCID,Landreman Matt1ORCID,Brunton Steven L.2ORCID

Affiliation:

1. Institute for Research in Electronics and Applied Physics, University of Maryland 1 , College Park, Maryland 20742, USA

2. Department of Mechanical Engineering, University of Washington 2 , Seattle, Washington 98195, USA

3. Department of Aeronautics and Astronautics, University of Washington 3 , Seattle, Washington 98195, USA

4. Department of Applied Physics and Applied Mathematics, Columbia University 4 , New York, New York, 10027, USA

5. Oak Ridge National Laboratory 5 , Oak Ridge, Tennessee 37831, USA

Abstract

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.

Funder

U.S. Department of Energy

Simons Foundation

National Science Foundation

Publisher

AIP Publishing

Subject

Condensed Matter Physics

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