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Abstract The finite‐difference time‐domain (FDTD) method was previously applied to high‐frequency electromagnetic wave propagation through 250 km of theFregion of the ionosphere. That modeling approach was limited to electromagnetic wave propagation above the critical frequency of the ionospheric plasma, and it did not include the lower ionosphere layers or the top of theF‐region. This paper extends the previous modeling methodology to frequencies below the critical frequency of the plasma and to altitudes encompassing the ionosphere. The following changes to the previous work were required to generate this model: (a) theD,Eand top of theFregions of the ionosphere were added; and (b) the perfectly matched layer absorbing boundary on the top side of the grid was replaced with a collisional plasma to prevent reflections. We apply this model to the study of extremely low frequency (ELF) and very low frequency (VLF) electric power line harmonic radiation (PLHR) through the ionosphere. The model is compared against analytical predictions and applied to PLHR propagation in polar, mid‐latitude and equatorial regions. Also, to further demonstrate the advantages of the grid‐based FDTD method, PLHR propagation through a polar cap patch with inhomogeneities is studied. The presented modeling methodology may be applied to additional scenarios in a straightforward manner and can serve as a useful tool for better tracking and studying electromagnetic wave propagation through the ionosphere at any latitude and in the presence of irregularities of any size and shape.
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FDTD-Net: A Deep Learning technique to model the FDTD Method
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.
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- Award ID(s):
- 1750970
- PAR ID:
- 10513263
- Publisher / Repository:
- Latin American and Caribbean Consortium of Engineering Institutions
- Date Published:
- Journal Name:
- 21 st LACCEI International Multi-Conference for Engineering, Education, and Technology: “Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development”
- ISSN:
- 2414-6390
- ISBN:
- 978-628-95207-4-3
- Subject(s) / Keyword(s):
- DTD method U-net model Encoder-Decoder
- Format(s):
- Medium: X
- Location:
- Buenos Aires
- Sponsoring Org:
- National Science Foundation
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