Probabilistic risk assessment and computational stochasitc mechanics

Roger Ghanem is a professor in the department of aerospace and mechanical engineering and the department of civil engineering in the USC Viterbi School of Engineering. His research interests include probabilistic risk assessment and computational stochasitc mechanics, inluding optimization under uncertainty, model validation, and multiscale modeling.

Ghanem serves on the editorial boards of Probabilistic Engineering Mechanics and the International Journal of Multiscale Computational Engineering. He is also the chair of the executive committee of the Engineering Mechanics Division of the American Society of Civil Engineers (ASCE). He is an elected fellow of the U.S. Association of Computational Mechanics (USACM) and a recipient of the ASCE Walter L. Huber Civil Engineering Research Prize and the International Association for Structural Safety and Reliability Junior Research Prize.

Ghanem is coauthor of Stochastic Finite Elements: a Spectral Approach (Dover Publications), a widely recognized guide to the analysis and design of random systems. Together with collaborators, he has applied spectral projection methods to complex problems in science and engineering, including, among others, microfluidics, protein labeling, structural acoustics and dynamics, reactive flows, multiphase flows in porous media, the human liver, and prognosis. His work is supported by the National Science Foundation, the Air Force Office of Scientific Research, and the Department of Energy Laboratories.

An essential problem addressed by Ghanem and his group involves making inferences about the performance of engineered and natural systems under constraints of limited observational data. This task requires significant computational resources, because the task of constructing probabilistic estimates of spatially varying system parameters involves optimization under uncertainty in a very high-dimensional space. Procedures must be developed that guide the analyst as to how to best allocate resources among numerical resolution, modeling resolution, and experimental resolution. The algorithms and theories developed by G hanem and his group, which have been integrated into terascale platforms used at Sandia National Laboratories, have been deployed in the HPCC environment at USC. These tools provide the software infrastructure to solve problems of stochastic prediction, stochastic inversion, and optimization on large-scale computational platforms with minimal supervision on the analyst's part.