Prof. Qiong Wang Profile

Prof. Qiong Wang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 28 March 2024 Paper

Predicting electromagnetic response of electromagnetic metasurface using deep learning

Bingyan Yang, Qiong Wang, Lei Mu

Proceedings Volume 13091, 130912I (2024) https://doi.org/10.1117/12.3023137

KEYWORDS: Electromagnetism, Education and training, Neural networks, Design, Deep learning, Machine learning, Deep convolutional neural networks, Data modeling, Matrices, Binary data

Read Abstract +

Digitally coded metasurface (DMS) is a novel class of electromagnetic materials that possess the ability to manipulate electromagnetic waves at scales significantly smaller than the wavelength. They hold great potential for a wide range of applications, including wireless communication, millimeter-wave imaging, data storage, and sensing. However, the conventional full-wave simulation methods currently employed often require precise geometric modeling, grid planning, and consideration of complex physical models such as material parameters and boundary conditions. These time-consuming and costly approaches face significant challenges in large-scale electromagnetic response studies. Therefore, there is an urgent need to explore more efficient and cost-effective methods to address these issues. In this paper, we propose a deep learning-based approach for predicting the electromagnetic response of individual units in a DMS. We construct a convolutional neural network (CNN) model that can accurately and real-time predict the electromagnetic response based on the input coding pattern. To train and validate the model, we utilize a substantial amount of electromagnetic simulation data and incorporate amplitude constraints into the loss function for model optimization. A way to abandon the dual network structure and achieve simultaneous prediction of dual parameters with smaller computational requirements. Experimental results demonstrate that our model achieves highly accurate predictions of both amplitude and phase responses of DMS, surpassing traditional numerical methods in terms of efficiency and scalability. The proposed deep learning approach offers a promising solution for efficient and low-cost prediction of electromagnetic responses in DMS.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years