Leveraging turbulence data from fusion experiments

TL;DR

This paper introduces various methods, including spectral, statistical, and neural network approaches, for analyzing turbulence data from fusion plasma experiments to enhance understanding of plasma turbulence transport.

Contribution

It presents a comprehensive overview of methods for leveraging turbulence measurements, emphasizing their application to two-dimensional data in fusion experiments.

Findings

Spectral, statistical, and neural network methods are effective for 2D turbulence analysis.

Applying these methods can improve understanding of plasma turbulence transport.

Selected examples demonstrate practical applications of the methods.

Abstract

Various methods for leveraging turbulent fluctuation measurements from fusion plasma experiments are introduced, along with selected application examples. These can be categorized into spectral methods, statistical methods, and physics informed neural network based methods, and they are most effective for two-dimensional turbulence measurements, which are now widely accessible. Extracting more information from turbulence data would pave the way for a better understanding of plasma turbulence transport in fusion experiments.