Machine Learning Based Forward Solver: An Automatic Framework in gprMax

Akhaury, Utsav, Giannakis, Iraklis, Warren, Craig and Giannopoulos, Antonios (2021) Machine Learning Based Forward Solver: An Automatic Framework in gprMax. In: 2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR). IEEE, Piscataway, pp. 1-6. ISBN 9781665446624, 9781665422536

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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.

Item Type: Book Section
Additional Information: Funding information: The project was funded via the Google Summer of Code (GSoC) 2021 programme. GSoC initiative is a global program focused on bringing student developers into open source software development. The source code for this project can be found at .
Uncontrolled Keywords: Full-Waveform Inversion (FWI), Machine Learning (ML), Principle Component Analysis (PCA), Singular Value Decomposition (SVD), Random Forest, XGBoost (Extreme Gradient Boosting)
Subjects: G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Elena Carlaw
Date Deposited: 01 Sep 2022 16:03
Last Modified: 01 Sep 2022 16:15

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