Events

Position : Home > Events > Content

(July 30)The Future of Optimization in Electromagnetics

Time: Aug 29, 2016

时间 地点
人物

Title:

The  Future of Optimization in Electromagnetics

Lecturer:

Prof. Randy  L. Haupt

Time:

8:30, July 30,  2016

Venue:

412,  Section III, West Building, North Campus

Lecturer  Profile

Randy  L. Haupt received the BSEE from the USAF Academy (1978), the MS in Engineering  Management from Western New England College (1982), the MSEE from Northeastern  University (1983), and the PhD in EE from The University of Michigan (1987). He  is Professor EECS at the Colorado School of Mines. Prior to that, he was an RF  Staff Consultant at Ball Aerospace & Technologies, Corp., Senior Scientist  and Department Head at the Applied Research Laboratory of Penn State, Professor  and Department Head of ECE at Utah State, Professor and Chair of EE at the  University of Nevada Reno, and Professor of EE at the USAF Academy. He was a  project engineer for the OTH-B radar and a research antenna engineer for Rome  Air Development Center early in his career. He is co-author of the books  Practical Genetic Algorithms, 2 ed., John Wiley & Sons, 2004, Genetic  Algorithms in Electromagnetics, John Wiley & Sons, 2007, and Introduction  to Adaptive Antennas, SciTech, 2010, as well as author of Antenna Arrays a  Computation Approach, John Wiley & Sons, 2010. Dr. Haupt is a Fellow of the  IEEE, the Applied Computational Electromagnetics Society (ACES), and the  Electromagnetics Academy.

Lecture  Abstract

Not  many years ago, engineers and scientists were satisfied with finding a  solution. Computers and other technology advances encouraged us to build more  complex systems and models for those systems. The large number of design  variables associated with system design confounded traditional approaches to  optimizing a system or finding the best design. Derivative based optimization  approaches work best for small, linear systems. Large, nonlinear systems  motivated the more recent development of guided random guessing algorithms that  are modeled after natural processes. This talk starts with the foundations of  analytical optimization and moves on to the elegant derivative based algorithms  of the early computer age before ending with the guided random search  strategies of today. The talk ends with a status of optimization approaches for  electromagnetics and where the future lies.

【Close】