Machine Learning Exercises on One Dimensional Electromagnetic Inversion
Loading...
Links to Files
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2021-04-07
Type of Work
Department
Program
Citation of Original Publication
Simsek, Ergun; Machine Learning Exercises on One Dimensional Electromagnetic Inversion; IEEE Transactions on Antennas and Propagation (2021); https://ieeexplore.ieee.org/document/9398540
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Abstract
This work aims to enhance our fundamental understanding of how the measurement setup used to generate training and testing datasets affects the accuracy of the machine learning algorithms that attempt solving electromagnetic inversion problems solely from data. A systematic study is carried out on a one-dimensional semi-inverse electromagnetic problem, which is estimating the electrical permittivity values of a planarly layered medium with fixed layer thicknesses assuming different receiver-transmitter antenna combinations in terms of location and numbers. Accuracy of the solutions obtained with four machine learning methods including neural-networks is compared with a physics-based solver deploying the Nelder-Mead simplex method to achieve the inversion iteratively. Numerical results show that (i) deep-learning outperforms the other machine learning techniques implemented in this study, (ii) increasing number of antennas and placing them as close as possible to the domain of interest increase inversion accuracy, (iii) for neural networks, training datasets created on random grids lead to a more efficient learning than the training datasets created on uniform grids, and (iv) multi-frequency training and testing with a few antennas can achieve more accurate inversion than single-frequency setups deploying several antennas.