Impact of the Complexity of the Geometry in an Analytical Solution Used to Train a Deep Learning Network

Analytical solutions can be used to generate data to train Deep Learning neural networks to estimate electromagnetic fields. In this work, we compare the efficacy of two neural networks when two 2D analytical solutions are used to generate the training data: a geometry with 2 concentric infinitely long cylinders, and a geometry with up to 8 concentric infinitely long cylinders. The neural networks can estimate the electric fields and are trained with B1+ and SNR maps. The validation process was performed with results obtained with 3D numerical simulations. Even if more layers should provide higher heterogeneity in the training process, no significant improvement has been achieved with training with more layers, suggesting that it might be necessary to generate more data for better training with more heterogeneous geometries.