Advanced Simulation Datasets for Deep Learning-Based Photonic and Electromagnetic Research using FDTD Methods (Published)
We have provided finite numerical datasets using the FDTD technique, which describes the electromagnetic field distribution against the changes in material and structural characteristics in this paper. It holds information related to many numerical parameters and the field images of the corresponding shape and size for different configurations for Gold, MgF2, and glass. This dataset was created to enhance the study of photonics, optics, and electromagnetic waves and serve as an input for reinforcement learning models intended to make precise estimations of field behavior induced by material and/or geometrical inputs for photonics and optics. We also describe other datasets mentioned in the contextual literature and establish how our dataset is different by providing a more comprehensive, parameterized set of images and simulation data. Thus, describing the approach used to create the dataset, we discuss its possible use in various disciplines – from nanophotonic to machine learning where precise electromagnetic field modeling is needed.
Keywords: deep learning, electromagnetic field distribution, finite-difference time-domain (FDTD), parameterized dataset, photonics and plasmonics