Characterizing Magnetized Plasmas with Dynamic Mode Decomposition
Alan A. Kaptanoglu, Kyle D. Morgan, Chris J. Hansen, and Steven L., Brunton

TL;DR
This paper applies dynamic mode decomposition to magnetized plasma data, creating interpretable models that reveal physical structures and instabilities, balancing accuracy and computational efficiency.
Contribution
It introduces the use of DMD variants for plasma analysis, uncovering new physical insights and structures in experimental and simulation data.
Findings
Identified coherent magnetic modes in plasma data.
Discovered a new three-dimensional plasma structure.
Detected a resistive kink mode in experimental data.
Abstract
Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear, computationally intensive, and can be difficult to interpret, while often only approximating the true dynamics. In this work, data-driven techniques recently developed in the field of fluid dynamics are leveraged to develop interpretable reduced-order models of plasmas that strike a balance between accuracy and efficiency. In particular, dynamic mode decomposition (DMD) is used to extract spatio-temporal magnetic coherent structures from the experimental and simulation datasets of the HIT-SI experiment. Three-dimensional magnetic surface probes from the HIT-SI experiment are analyzed, along with companion simulations with synthetic internal magnetic probes. A number of leading variants of the DMD algorithm are compared,…
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