Manifold learning: A promising tool for knowledge discovery and inverse design in nanophotonics

Mohammadreza Zandehshahvar; Yashar Kiarashi; Muliang Zhu; Tyler Brown; Mohammad Hadighe Javani; Ali Adibi

doi:10.1117/12.2617486

5 March 2022 Manifold learning: A promising tool for knowledge discovery and inverse design in nanophotonics

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Tyler Brown, Mohammad Hadighe Javani, Ali Adibi

Proceedings Volume PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII; PC120100U (2022) https://doi.org/10.1117/12.2617486
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

Here, we present a new approach based on manifold learning for inverse design and knowledge discovery in nanophotonics. We present the unique capabilities of manifold learning approaches for reducing the dimensionality of the high-dimensional relationships in photonic nanostructures. We show how this can help to understand the underlying patterns in the responses of such nanostructures. Such a visualization in the low-dimensional space enables knowledge discovery and studying the underlying physics of nanostructures and can facilitate the inverse design. We also use this method to study the role of the design parameters and design a class of nanostructure while reducing the design complexity.

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Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Tyler Brown, Mohammad Hadighe Javani, and Ali Adibi "Manifold learning: A promising tool for knowledge discovery and inverse design in nanophotonics", Proc. SPIE PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII, PC120100U (5 March 2022); https://doi.org/10.1117/12.2617486

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KEYWORDS

Nanophotonics

Knowledge discovery

Nanostructures

Artificial intelligence

Physics

Genetic algorithms

Light-matter interactions

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