Revealing the hidden capacity of artificial intelligence in nanoscience: physics-driven metric learning

Hossein Maleki; Mohammadreza Zandehshahvar; Yashar Kiarashi; Muliang Zhu; Ali Adibi

doi:10.1117/12.2590756

5 March 2021 Revealing the hidden capacity of artificial intelligence in nanoscience: physics-driven metric learning

Hossein Maleki, Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Ali Adibi

Proceedings Volume 11694, Photonic and Phononic Properties of Engineered Nanostructures XI; 116940O (2021) https://doi.org/10.1117/12.2590756
Event: SPIE OPTO, 2021, Online Only

Abstract

We present a new approach for design of novel loss functions and introduce an optimal similarity-metric design for machine-learning-based design and knowledge discovery in nanophotonics. Machine-learning algorithms estimate the input-output relation in a photonic nanostructure by minimizing a loss function. We show that careful selection (from the available loss functions) or design of novel loss functions that are optimized for specific tasks can considerably improve the performance of machine-learning algorithms for design and knowledge discovery in photonic nanostructures. We also discuss the limitations and inefficacies of conventional loss functions that are currently being used for machine learning algorithms.

Conference Presentation

Citation Download Citation

Hossein Maleki, Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, and Ali Adibi "Revealing the hidden capacity of artificial intelligence in nanoscience: physics-driven metric learning", Proc. SPIE 11694, Photonic and Phononic Properties of Engineered Nanostructures XI, 116940O (5 March 2021); https://doi.org/10.1117/12.2590756

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available