Using Model-Based Parameter Estimation to Increase the Efficiency and Effectiveness of Computational Electromagnetics
Edmund K. Miller
Distinguished Lecturer, IEEE Antennas and Propagation Society
Date: 11:00 AM – 12:00 PM, Friday, May 27th, 2016
Place: ElectroScience Lab, The Ohio State University, MRC Conference Room #132, 1330 Kinnear Road, Columbus, OH 43212
Science began, and largely remains, an activity of making observations and/or collecting data about various phenomena in which patterns may be perceived and for which a theoretical explanation is sought in the form of mathematical prescriptions. These prescriptions may be non-parametric, first-principles generating models (GMs), such as Maxwell’s equations, that represent fundamental, irreducible descriptions of the physical basis for the associated phenomena. In a similar fashion, parametric fitting models (FMs) might be available to provide a reduced-order description of various aspects of the GM or observables that are derived from it. The purpose of this lecture is to summarize the development and application of exponential series and pole series as FMs in electromagnetics. The specific approaches described here, while known by various names, incorporate a common underlying procedure that is called model-based parameter estimation (MBPE)
An MBPE model of a frequency response can provide a continuous representation to a specified estimation error of a percent or so using 2 or even fewer samples per resonance peak, a procedure sometimes called a fast frequency sweep. Comparable performance can be similarly achieved using MBPE to model a far-field pattern. The adaptive approach can also yield an estimate of the data dimensionality or rank so that the FM order can be maintained below some threshold while achieving a specified FM accuracy. Topics to be discussed include: a preview of model-based parameter estimation; fitting models for waveform and spectral data; function sampling and derivative sampling; adaptive sampling of frequency spectra and far-field patterns; and using MBPE to estimate data uncertainty.
Since earning his PhD in Electrical Engineering at the University of Michigan, Edmund K. Miller has held a variety of government, academic and industrial positions. These include 15 years at Lawrence Livermore National Laboratory and 4+ years at Los Alamos National Laboratory His academic experience includes holding a position as Regents-Distinguished Professor at Kansas University, Stocker Visiting Professor at Ohio University, Physics Instructor at Michigan Technological University and Research Engineer at the University of MichiganDr. Miller has been appointed as an AP Distinguished Lecturer for 2014-2016, and wrote the columns “PCs for AP and Other EM Reflections” from 1984 to 2000 for the Magazine of the Antennas and Propagation Society. He received (with others) a Certificate of Achievement from the IEEE Electromagnetic Compatibility Society for Contributions to Development of NEC (Numerical Electromagnetics Code) and was a recipient (with others) in 1989 of the best paper award given by the Education Society for “Computer Movies for Education.