Data-Driven Shadowgraph Simulation of a 3D Object
This work provides a computationally cheaper method for plasma physics researchers to visualize perturbations, but it is incremental as it focuses on interpolation within a limited parameter set.
The authors tackled the problem of computationally expensive plasma shadowgraph simulations by developing a deep neural network surrogate model that approximates electric fields without needing full numerical simulations, achieving good reconstruction quality within a narrow parameter range.
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.