AI / ML for Photonics

Data-driven methods of machine learning (ML) have attracted a lot of interest in various fields of physics. Inverse design and optimisation of structured optical metamaterials such as photonic crystals, metasurfaces, and other nanostructured components seem to benefit a lot from this approach in the nearest future. We develop several approaches to use ML methods to effectively predict and optimise properties of photonic crystals and metamaterials (e.g. size of photonic bandgaps, effective medium parameters, photonic band diagrams). For inverse design, we use generative ML models such as Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), and also are investigating other newest developments in this field.

Our recent research is outlined in a special issue on Inverse Design of nanophotonics devices and materials (see also the open-access early version of this work) and future projects are related to 3D extensions of this research and applications of various ML methods to the data from the newest databases of photonic crystals (see e.g. the Photonic Structure Database or this Nature article).

If you are interested in research in this direction, you can also have a look at other related publications, those results we will use intensively in the future: see e.g. this Nature article and references therein. We always need a help from enthusiastic students, and have various possible projects to be involved in.

Alexander Itin
Senior Researcher

Interdisciplinary researcher (applied mathematics, physics, computer science) with passionate curiosity. PhD in theoretical physics with extensive additional training in machine learning, bioinformatics, graph theory. Experience in signal processing and data analysis, application of machine learning techniques to scientific and industrial projects.