Fundamentals of Uncertainty Quantification for Engineers

Dr. Yan Wang is a Professor of Mechanical Engineering at the Georgia Institute of Technology. He leads the Multiscale Systems Engineering Research Group at Georgia Tech. His research interests include probabilistic and non-probabilistic approaches to quantify uncertainty in both physics-based and data-driven models for multiscale systems engineering for materials design. He has over 200 publications, including the first book on uncertainty quantification in multiscale materials modelling co-edited with David McDowell. Dr. Anh V. Tran is a research staff member at the Department of Scientific Machine Learning, Sandia National Laboratories. His research areas include uncertainty quantification, optimization, machine learning for multiscale computational materials science. David L. McDowell Ph.D. is Regents’ Professor Emeritus at the Georgia Institute of Technology, having joined Georgia Tech as a faculty member in 1983. His research focuses on multiscale modelling of materials with emphasis on multiscale modeling of the inelastic behavior of metals, microstructure-sensitive computational fatigue analysis of microstructures, methods for materials design that are robust against uncertainty, and coarse-grained atomistic modelling methods.

1. Introduction to Uncertainty Quantification for Engineers
2. Probability and Statistics in Uncertainty Quantification
3. Random Processes in Uncertainty Quantification
4. Sampling Methods in Uncertainty Quantification
5. Surrogate Modeling in Uncertainty Quantification
6. Model Selection, Calibration, and Validation in Uncertainty Quantification
7. Sensitivity Analysis in Uncertainty Quantification
8. Stochastic Expansion Methods in Uncertainty Quantification
9. Markov Models
10. Non-Probabilistic Methods in Uncertainty Quantification
11. Uncertainty propagation in Uncertainty Quantification