We propose a 3D U-Net model to predict the spatial distribution of electromagnetic fields inside a radio-frequency (RF) coil with a subject present, using the phase, amplitude, and position of the coils, along with the density, permittivity, and conductivity of the surrounding medium as inputs. To improve accuracy, we introduce a physics-augmented variant, U-Net Phys, which incorporates Gauss's law of magnetism into the loss function using finite differences. We train our models on electromagnetic field simulations from CST Studio Suite for an eight-channel dipole array RF coil at 7T MRI. Experimental results show that U-Net Phys significantly outperforms the standard U-Net, particularly in predicting fields within the subject, demonstrating the advantage of integrating physical constraints into deep learning-based field prediction.
View on arXiv@article{dulny2025_2503.11703,
title={ Physical knowledge improves prediction of EM Fields },
author={ Andrzej Dulny and Farzad Jabbarigargari and Andreas Hotho and Laura Maria Schreiber and Maxim Terekhov and Anna Krause },
journal={arXiv preprint arXiv:2503.11703},
year={ 2025 }
}