High-Accuracy Prediction of Metal-Insulator-Metal Metasurface with Deep Learning. (arXiv:2308.04450v1 [cs.LG])

Deep learning prediction of electromagnetic software calculation results has
been a widely discussed issue in recent years. But the prediction accuracy was
still one of the challenges to be solved. In this work, we proposed that the
ResNets-10 model was used for predicting plasmonic metasurface S11 parameters.
The two-stage training was performed by the k-fold cross-validation and small
learning rate. After the training was completed, the prediction loss for
aluminum, gold, and silver metal-insulator-metal metasurfaces was -48.45,
-46.47, and -35.54, …

accuracy arxiv challenges deep learning issue metal prediction software stage training work