Identifying topology of leaky photonic lattices with machine learning

Identifying topology of leaky photonic lattices with machine learning

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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 an approach based solely on a single telemarkskongen flue real-space bulk intensity image, thus exempt from complicated phase retrieval procedures.In osborne hog feeders for sale particular, we design a fully connected neural network that accurately determines topological 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.