報告題目: Stochastic Inversion arising in engineering sciences - classical and modern approaches
報告人：Dr. Fumio Kojima（Kobe University）
報告時間：2016年11月29日10:00
報告地點：18-529
主辦單位：國際交流合作處、航空宇航學院、機械結構力學及控制國家重點實驗室、校科協
Bibliography:
Dr. Fumio Kojima received the Doctor of Engineering from Kyoto University in 1985. He was a staff scientist at Institute of Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, Virginia during 1986-90. In 1990, he was an assistant professor at Research Center in Applied Mathematics (RIMS) in the University of Southern California. In 1991 he was an associate professor at the Department of Mechanical Engineering, Osaka Institute of Technology and became a professor in 1994. In 1999, he joined Graduate School of Science and Technology, Kobe University. He retired his position at Kobe University this year. He is currently Emeritus Professor of Kobe University, Visiting Professor at Tohoku University, and at Tokyo Metropolitan University. He was also scientific consultant at ICASE during 1990-92 and had visiting positions in North Carolina State University (CRSC), University of Minnesota (IMA), Chinese University of Hong Kong, Xian Jiaotong University, etc. He has been a scientific advisor for the Institute of Applied Mechanics at Vietnamese Academy of Science and Technology since 2005. He has been also an editor of Electrical Journal of Advanced Maintenance (E-JAM) since 2009, a president of the Institute of Systems, Control and Information Engineers (ISCIE) during 2013 to 2014 and a fellow of Japan Society of Mechanical Engineers (JSME) and ISCIE.
Abstract:
One of the fundamental reasons for examining any physical system by mathematical analysis is able to predict the behavior of the system, when the physical properties, boundary conditions and initial conditions are known. Quite often, part of the required data, initial and/or boundary states are not known and the system itself must be sampled experimentally in order to obtain enough information to determine the unknown system parameters. Such problems have been treated as inverse problems that cover variety of applications in science and engineering. This lecture is concerned with stochastic inversion techniques for classical method and for the recent statistical method. The first part is devoted to a method of parameter estimation for a stochastic wave equation arising in oil exploration problems. The second part using a new inverse methodology is directed to electromagnetic propagation in dielectric medium including application to detection of cable degradation.
