Identifying topology of leaky photonic lattices with machine learning

Identifying topology of leaky photonic lattices with machine learning

Blog Article

We show how machine learning techniques can be applied for the classification of topological phases in finite leaky photonic lattices using limited measurement data.We propose oas ba?adores an approach based solely on a single real-space bulk intensity image, thus exempt from complicated phase retrieval procedures.In particular, we design a fully connected neural network that accurately determines topological 53-264817 properties from the output intensity distribution in dimerized waveguide arrays with leaky channels, after propagation of a spatially localized initial excitation at a finite distance, in a setting that closely emulates realistic experimental conditions.