Department of Civil Engineering, Johns Hopkins University
Advances in Simulation-Based Uncertainty Quantification and Reliability Analysis
Wednesday, Nov. 18th, 10:00 – 11:00am,
G31 Benedum Hall
Abstract: Modern design and analysis of structures (civil, aerospace, naval, automotive, etc.) relies heavily on computational modeling and simulation. Assessment of structural safety and reliability requires integration of uncertainties in the physical system, its excitation/load, and the model itself into the design and analysis process. These uncertainties can be difficult to quantify and, once quantified, can be computationally expensive to propagate. This presentation offers a survey of new probabilistic methods being developed by the Shields Uncertainty Research Group (SURG) at Johns Hopkins University for the computationally efficient quantification and propagation of uncertainties through large and complex computational models for the assessment of structural safety and reliability.
Bio: Michael Shields is an Assistant Professor in the Department of Civil Engineering at Johns Hopkins University. Prof. Shields conducts methodological research in uncertainty quantification and stochastic simulation for problems in computational mechanics ranging from the characterization and propagation of uncertainty across length and time-scales in material modeling to assessing the probabilistic response of large-scale structures under uncertainty (e.g. buildings and bridges subject to wind or seismic load, ships subject to uncertain sea states or extreme loads, etc.). He received his Ph.D. in Civil Engineering and Engineering Mechanics from Columbia University in 2010 after which he was employed as a Research Engineer in applied computational mechanics at Weidlinger Associates, Inc. in New York City. Prof. Shields holds a joint appointment in the Department of Materials Science and Engineering and is also a faculty member of the Hopkins Extreme Materials Institute, the Center for Integrated Structures and Material Modeling and Simulation, and the Systems Institute at Johns Hopkins University.