Title: Data-Driven Shadowgraph Simulation of a 3D Object Show Chinese title

Title: 数据驱动的三维物体阴影图模拟

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. Show Chinese abstract

Abstract: 在本工作中，我们提出了一种基于深度神经网络的代理模型，用于等离子体阴影图——一种用于可视化透明介质中扰动的技术。 我们将数值代码替换为计算成本更低的基于投影的代理模型，该模型能够在不计算数值方法所需的全部先前电场的情况下，近似给定时间的电场。 这意味着基于投影的代理模型能够在不需要运行完整模拟的情况下，在给定计算域和配置的任何点恢复控制的三维偏微分方程，即三维波动方程的解。 该模型在仿真参数的狭窄范围内的数据插值问题中表现出良好的重建质量，并可用于大规模输入数据。