ArticleNanophotonics (Berlin, Germany)2024
Identifying topology of leaky photonic lattices with machine learning.
Article in Nanophotonics (Berlin, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Quantum circuit complexity and unsupervised machine learning of topological order.Nature communications · 2026Article
- Deep Learning-Assisted Design for High-Q-Value Dielectric Metasurface Structures.Materials (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
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 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 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.
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