A Machine Learning Perspective on Spectral Estimation Techniques for Time Signals From Electromagnetic Field Simulations

We propose a machine-learning-based workflow to solve the spectral estimation problem of time signals from electromagnetic simulations of highly resonant devices. An activation function for a three-layer radial basis function network is defined and used to fit the signal originated from a grid-based computation. Due to the proper choice of the activation function and a careful minimization of the loss expression, we are able to train this network using only a small part of the original time signal and finally extrapolate the signal up to a point it has sufficiently decayed. In the end, we obtain an estimation of the signal’s spectral content which is considerably more accurate than a Fourier transform of the training data alone. Therefore, using our approach one may abort a time domain simulation earlier, and still calculate important key quantities such as resonance frequencies and
$Q$
-values with high precision. In order to test our method, we perform the spectral estimation of a highly resonant filter’s response.