Data-Driven Shadowgraph Simulation of a 3D Object

TL;DR

This paper introduces a deep neural network surrogate model for plasma shadowgraph visualization, enabling fast approximation of electric fields in 3D without full numerical simulations, thus saving computational resources.

Contribution

The work presents a novel projection-based deep learning surrogate model that approximates 3D electric fields efficiently, bypassing traditional numerical PDE solvers.

Findings

Model accurately reconstructs electric fields within a narrow parameter range.

Surrogate model significantly reduces computational costs compared to numerical methods.

Effective for large input data sizes in 3D shadowgraph simulations.

Abstract

In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium. We are substituting the numerical code by a computationally cheaper projection based surrogate model that is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This means that the projection based surrogate model allows to recover the solution of the governing 3D partial differential equation, 3D wave equation, at any point of a given compute domain and configuration without the need to run a full simulation. This model has shown a good quality of reconstruction in a problem of interpolation of data within a narrow range of simulation parameters and can be used for input data of large size.