Abstract
This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates robust short-term forecasting.
Recommended Citation
Y. Zhang et al., "A Data-Driven Approach to Time-Domain Electromagnetic Modeling based on Dynamic Mode Decomposition," 2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024 - Proceedings, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/ACES-China62474.2024.10699586
Department(s)
Geosciences and Geological and Petroleum Engineering
Second Department
Electrical and Computer Engineering
Keywords and Phrases
data-driven approach; dynamic mode decomposition; finite-difference time-domain method; Time-domain electromagnetic modeling
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Included in
Electrical and Computer Engineering Commons, Geology Commons, Geophysics and Seismology Commons
