Understanding the surface wave characteristics using 2D particle-in-cell simulation and deep neural network

TL;DR

This paper investigates plasma-dielectric surface waves using 2D particle-in-cell simulations and employs deep neural networks to predict radiation pressure, aiming to enhance data-driven plasma device design.

Contribution

It combines kinetic PIC simulations with deep neural networks to analyze and predict surface wave characteristics in plasma-dielectric interfaces, a novel integration.

Findings

Surface waves are excited along plasma-dielectric interfaces with identifiable electric field patterns.

Radiation pressure depends on dielectric permittivity and input frequency.

Deep neural networks can accurately predict radiation pressure in these systems.

Abstract

The characteristics of the surface waves along the interface between a plasma and a dielectric material have been investigated using kinetic Particle-In-Cell (PIC) simulations. A microwave source of GHz frequency has been used to trigger the surface wave in the system. The outcome indicates that the surface wave gets excited along the interface of plasma and the dielectric tube and appears as light and dark patterns in the electric field profiles. The dependency of radiation pressure on the dielectric permittivity and supplied input frequency has been investigated. Further, we assessed the capabilities of neural networks to predict the radiation pressure for a given system. The proposed Deep Neural Network model is aimed at developing accurate and efficient data-driven plasma surface wave devices.