Machine Learning Based Forward Solver: An Automatic Framework in gprMax

• URL: http://arxiv.org/abs/2111.12148v1
• Date: Tue, 23 Nov 2021 20:46:21 GMT
• Title: Machine Learning Based Forward Solver: An Automatic Framework in gprMax
• Authors: Utsav Akhaury, Iraklis Giannakis, Craig Warren, Antonios Giannopoulos
• Abstract summary: General full-wave electromagnetic solvers 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. We have developed a framework that is capable of generating these ML-based forward solvers automatically.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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