General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. To ease the process, we have developed a framework that is capable of generating these ML-based forward solvers automatically. The framework uses an innovative training method that combines a predictive dimensionality reduction technique and a large data set of modeled GPR responses from our FDTD simulation software, gprMax. The forward solver is parameterized for a specific GPR application, but the framework can be extended in a straightforward manner to different electromagnetic problems.
Cite
@article{arxiv.2111.12148,
title = {Machine Learning Based Forward Solver: An Automatic Framework in gprMax},
author = {Utsav Akhaury and Iraklis Giannakis and Craig Warren and Antonios Giannopoulos},
journal= {arXiv preprint arXiv:2111.12148},
year = {2021}
}