TY - GEN
T1 - Machine Learning Based Forward Solver
T2 - An Automatic Framework in gprMax
AU - Akhaury, Utsav
AU - Giannakis, Iraklis
AU - Warren, Craig
AU - Giannopoulos, Antonios
N1 - 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 https://github.com/gprMax/gprMax/pull/294 .
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Full-Waveform Inversion (FWI)
KW - Machine Learning (ML)
KW - Principle Component Analysis (PCA)
KW - Singular Value Decomposition (SVD)
KW - Random Forest
KW - XGBoost (Extreme Gradient Boosting)
U2 - 10.1109/iwagpr50767.2021.9843172
DO - 10.1109/iwagpr50767.2021.9843172
M3 - Conference contribution
SN - 9781665446624
BT - 2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)
PB - IEEE
CY - Piscataway
ER -