Investigating the Use of Physics-Endowed Machine Learning for Pole/Residue Extraction

A machine learning approach is investigated to identify poles of an arbitrary frequency response. The latent variables of a non-symmetrical auto-encoder are constrained to represent system poles, whereas residues are obtained using a least-squares approach. Various training regimes are tested.