Abstract
This paper proposes a novel method by rethinking the method of moments (MoM) solving process into a machine learning training process. Based on the artificial neural network (ANN), the conventional MoM matrix is treated as the training data set, based on which machine learning training process becomes conventional linear algebra MoM solving process. The trained result is the solution of MoM. The multiple linear regression (MLR) is utilized to train the model. Amazon Web Service (AWS) is used as the computations platform to utilize the existing hardware and software resources for machine learning. To verify the feasibility of the proposed new machine learning based method of moments (ML-MoM), we choose the static parasitic capacitance extraction and dynamic electromagnetics scattering as examples. The proposed novel idea opens a new gateway between conventional computational electromagnetics and machine learning algorithms with various application potentials.
Recommended Citation
H. M. Yao et al., "Machine Learning Based Method Of Moments (ML-MoM)," 2017 IEEE Antennas and Propagation Society International Symposium, Proceedings, pp. 973 - 974, Institute of Electrical and Electronics Engineers, Oct 2017.
The definitive version is available at https://doi.org/10.1109/APUSNCURSINRSM.2017.8072529
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Artificial Neural Network; Capacitance Extraction; Electromagnetic Scattering; Machine Learning; MoM
International Standard Book Number (ISBN)
978-153863284-0
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
18 Oct 2017
