@inproceedings{d084d7c249ba4cefa06416486a2e972b,
title = "Physics-Embedded Machine Learning for Efficient Modeling of High-Frequency Circuits",
abstract = "We propose the integration of analytical extension of eigenvalues (AEE) into neural network (NN) for efficient modeling of microwave circuits. By embedding the physics knowledge of lumped equivalent circuits, the proposed machine learning method allows us to build more generalizable relations between the input parameters of a circuit and its electromagnetic properties, showing a 10-fold improvement for input parameters outside the range of training set. The proposed model is also more data efficient in that it can achieve the same level of accuracy with fewer data compared with traditional NN-based circuit modeling.",
author = "Yanan Liu and Hongliang Li and Jin, {Jian Ming}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 ; Conference date: 10-07-2022 Through 15-07-2022",
year = "2022",
doi = "10.1109/AP-S/USNC-URSI47032.2022.9886657",
language = "English (US)",
series = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "834--835",
booktitle = "2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",
address = "United States",
}