Active Physics Informed Deep Learning: Surrogate Modeling for Non Planar Wavefront Excitation of Topological Nanophotonic Devices

Abstract

Topological plasmonics offers new ways to manipulate light by combining concepts from topology and plasmonics, similar to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high dimensional design space. We present a novel method that uses supervised, physics informed deep learning and surrogate modeling to design topological devices for specific wavelengths. By embedding physical constrains in the neural network training, our model efficiently explores the design space, significantly reducing simulation time. Additionally, we use non planar wavefront excitations via electron beams to probe topologically protected plasmonic modes, making the design and training process nonlinear. Using this approach, we design a topological device with unidirectional edge modes in a ring resonator at specific operational frequencies. Our method reduces computational cost and time while maintaining high accuracy, highlighting the potential of combining machine learning and advanced techniques for photonic device innovation.