The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning

Viktor A. Podolskiy; Sean Lynch; Jacob LaMountain; Jie Bu; Bo Fan; Amogh Raju; Anuj Karpatne; Daniel Wasserman

doi:10.1117/12.3027630

3 October 2024 The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning

Viktor A. Podolskiy, Sean Lynch, Jacob LaMountain, Jie Bu, Bo Fan, Amogh Raju, Anuj Karpatne, Daniel Wasserman

Author Affiliations +

Proceedings Volume PC13109, Metamaterials, Metadevices, and Metasystems 2024; PC1310908 (2024) https://doi.org/10.1117/12.3027630
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We aim to address one of the fundamental limitations of machine learning (ML): its reliance on extensive training datasets by incorporating physics-based intuition and Maxwell-equation-based constraints into ML process. We show that physics-guided networks require significantly smaller datasets, enable learning outside the original training data, and provide improved prediction accuracy and physics consistency. The proposed approaches are illustrated on examples of photonic composites, from photonic crystals to hyperbolic metamaterials.

Conference Presentation

Citation Download Citation

Viktor A. Podolskiy, Sean Lynch, Jacob LaMountain, Jie Bu, Bo Fan, Amogh Raju, Anuj Karpatne, and Daniel Wasserman "The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning", Proc. SPIE PC13109, Metamaterials, Metadevices, and Metasystems 2024, PC1310908 (3 October 2024); https://doi.org/10.1117/12.3027630

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