Uncovering turbulent plasma dynamics via deep learning from partial observations

TL;DR

This paper demonstrates that physics-informed neural networks can accurately learn turbulent plasma fields from limited observational data, advancing understanding and diagnostics of edge plasma turbulence in fusion reactors.

Contribution

It introduces a novel application of physics-informed neural networks constrained by PDEs to model plasma turbulence from partial data, improving upon traditional equilibrium models.

Findings

Neural networks accurately learn turbulent fields from partial observations.

The approach aligns with two-fluid plasma theory.

Potential for improved plasma diagnostics and theory validation.

Abstract

One of the most intensely studied aspects of magnetic confinement fusion is edge plasma turbulence which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, we demonstrate that physics-informed neural networks constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from just partial observations of a synthetic plasma's electron density and temperature in contrast with conventional equilibrium models. These techniques present a novel paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.