Modeling uncertainty and risk, random variables, modeling and simulation of functions of random variables and random processes, propagation of uncertainties in computational models, analytical and computational methods for computing failure probability of engineering systems, Bayesian updating of risk measures, communicating uncertainty and risk. Prerequisite: STAT 151 or equivalent.
Open to Continuing Ed students and non-engineering students with instructor permission
Safe and reliable performance of engineering systems under a variety of uncertain and extreme working conditions is of enormous importance to society, especially considering the consequences associated with such failures. Energy systems, transportation systems, infrastructure systems, computer networks, just to name a few, are examples of such systems. Many of these systems operate interdependently, cut across disciplinary boundaries and require advanced computational methods to evaluate their performance. As engineers we are interested not only in computing their response under assumed conditions, but more importantly, in modeling and quantifying the uncertainty associated with their performance and assessing the risk associated with failure. The two main themes of the course will be: 1) Uncertainty modeling, quantification and propagation using computational models, and 2) Assessment of risk taking into account technical, social and environmental factors.
1.Review fundamentals of probability theory and probability as logic 2.Provide tools for modeling and simulating random variables and random processes 3.Provide numerical methods for modeling and propagating uncertainties in engineering systems. 4.Provide methods to compute the probability of failure and reliability of engineering systems and components 5.State-of-the-art methods to update system reliability based on operational data 6. Define and quantify risk taking into account technical, social and environmental aspects
Assignments 20% Final Research Project 30% Midterm 25% Final 25%
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