Machine learning inverse problem for topological photonics.

The rapidly growing interest in the field of topological photonics is leading to the study of more and more complex structures to explore the properties of topological insulators.
This asks for a technique of properties computation speed and reliable. More challenging is to achieve an effective design methodology capable of solving the inverse problem in which desired optical properties result from topological characteristics.
Although various computational techniques are available for this process these tend to require specific implementations tailored to the task at hand.
Machine learning (ML) algorithms can give the right tools to do this and their recent growth in sophistication and application offers exciting perspectives on applying them to the field of topological photonics.
We apply ML techniques to solve the inverse problem of designing 1D photonic topological insulators, modelling the multidimensional nonlinear relationships among the structure parameters, whose custom tailoring can enable enhanced innovative applications to be realized.
We show that the architecture of ML with an appropriate label’s choice makes it possible to handle multivalued scenarios in different regimes of the modelling problem.