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8 June 2024 New machine learning paradigms for knowledge discovery and inverse design of photonic nanostructures
Ali Adibi, Mohammadreza Zandehshahvar, Mohammad Hadighehjavani, Yashar Kiarashi, Mahmoodreza Marzban, Mohammad R. Tavakol Harandi
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Proceedings Volume PC13030, Image Sensing Technologies: Materials, Devices, Systems, and Applications XI; PC130300C (2024) https://doi.org/10.1117/12.3013800
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
Abstract
Machine-learning algorithms are powerful tools in developing reliable models to relate the design space of a nanophotonic structure to its response space. They can be used not only to simplify the inverse design problem but also to provide valuable insight about the physics of light-matter interaction. This talk will provide a new approach through combining manifold-learning algorithms for reducing the dimensionality of the problem with metric-learning techniques for more insightful mapping of the input-output relation to the dimensionality-reduced (or the latent) space. In addition to covering the fundamental properties of the presented algorithms, their applications to both the inverse design and the knowledge discovery in state-of-the-art metaphotonic structures will be discussed.
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Ali Adibi, Mohammadreza Zandehshahvar, Mohammad Hadighehjavani, Yashar Kiarashi, Mahmoodreza Marzban, and Mohammad R. Tavakol Harandi "New machine learning paradigms for knowledge discovery and inverse design of photonic nanostructures", Proc. SPIE PC13030, Image Sensing Technologies: Materials, Devices, Systems, and Applications XI, PC130300C (8 June 2024); https://doi.org/10.1117/12.3013800
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KEYWORDS
Design and modelling
Knowledge discovery
Machine learning
Photonic nanostructures
Algorithm development
Light-matter interactions
Nanophotonics
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