Transfer Learning for Modeling Plasmonic Nanowire Waveguides.

ABSTRACT: Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an alternative approach but is challenging to use due to inadequate generalization performance and the requirement of large sets of training data. Here, we overcome these constraints by proposing a transfer learning approach for modeling MNWs under the guidance of physics. We show that the basic knowledge of plasmon modes can first be learned from free-standing circular MNWs with computationally inexpensive data, and then reused to significantly improve performance in predicting waveguiding properties of MNWs with various complex configurations, ena

SUBMITTER: Luo A 

PROVIDER: S-EPMC9612048 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature