A Deep Learning Approach to Describing the Plasma Sheath

TL;DR

This paper employs physics-informed neural networks to model plasma sheaths, enabling efficient predictions across various parameters without requiring experimental data, thus advancing plasma physics modeling.

Contribution

It introduces a PINN-based method for modeling plasma sheaths that does not need data and can serve as an efficient surrogate for traditional models.

Findings

PINNs can accurately model plasma sheath profiles.

Once trained, PINNs predict sheath behavior efficiently across parameters.

The method reduces reliance on experimental or simulation data.

Abstract

Despite their ubiquity, the rich physics present in a plasma sheath has inhibited the development of a generally applicable description of this critical region. The present study utilizes a physics-informed neural network (PINN) to evaluate a hierarchy of models of the plasma sheath. Unlike traditional deep learning methods, PINNs use the governing PDEs to constrain the predictions of a neural network, and thus do not require any experimental or simulation data to train. In this work, we utilize a PINN to identify the parametric solution to fluid models of different physics fidelity of the plasma sheath. While the offline training time of the PINN is often longer than a traditional solver, once trained, the PINN is able to efficiently predict the sheath profiles across a broad range of parameter regimes, thus yielding an effective surrogate of the plasma sheath.