FDTD-Net: A Deep Learning technique to model the FDTD Method (#572)

Authors

Alvarez Navarro, Michael Andres

Arzuaga Cruz, Emmanuel

Sierra Gil, Heidy

Abstract

The Finite-Difference Time-Domain (FDTD) method is a numerical modeling technique used by researchers as one of the most accurate methods to simulate the propagation of an electromagnetic wave through an object over time. Due to the nature of the method, FDTD can be computationally expensive when used in complex setting such as light propagation in highly heterogenous object such as the imaging process of tissues. In this paper, we explore a Deep Learning (DL) model that predicts the evolution of an electromagnetic field in a heterogeneous medium. In particular, modeling for propagation of a Gaussian beam in skin tissue layers. This is relevant for the characterization of microscopy imaging of tissues. Our proposed model named FDTD-net, is based on the U-net architecture, seems to perform the prediction of the electric field (EF) with good accuracy and faster when compared to the FDTD method. A dataset of different geometries was created to simulate the propagation of the electric field. The propagation of the electric field was initially generated using the traditional FDTD method. This data set was used for training and testing of the FDTD-net. The experiments show that the FDTD-net learns the physics related to the propagation of the source in the heterogeneous objects, and it can capture changes in the field due to changes in the object morphology. As a result, we present a DL model that can compute a propagated electric field in less time than the traditional method.