Machine Learning Based Forward Solver: An Automatic Framework in gprMax

Utsav Akhaury, Iraklis Giannakis, Craig Warren, Antonios Giannopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
8 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)9781665422536
ISBN (Print)9781665446624
Publication statusPublished - 1 Dec 2021


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