Machine learning based method of moments (ML-MoM)
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), based on which machine learningTraining process becomes conventional linear algebra MoM solving process.
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.