Fundamentals of Uncertainty Quantification for Engineers
Methods and Models
Yan Wang, Anh.V. Tran & David L. Mcdowell
Fundamentals of Uncertainty Quantification for Engineers
Methods and Models
Yan Wang, Anh.V. Tran & David L. Mcdowell
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English
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Description
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. Dr. David L. McDowell is Regents’ Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing in the Woodruff School of Mechanical Engineering, with joint appointment in the School of Materials Science and Engineering at the Georgia Institute of Technology. His research focuses on multiscale modelling of materials, microstructure-sensitive computational fatigue analysis of microstructures, methods for materials design that are robust against uncertainty, and coarse-grained atomistic modelling methods.
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. Dr. David L. McDowell is Regents’ Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing in the Woodruff School of Mechanical Engineering, with joint appointment in the School of Materials Science and Engineering at the Georgia Institute of Technology. His research focuses on multiscale modelling of materials, microstructure-sensitive computational fatigue analysis of microstructures, methods for materials design that are robust against uncertainty, and coarse-grained atomistic modelling methods.
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. Dr. David L. McDowell is Regents’ Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing in the Woodruff School of Mechanical Engineering, with joint appointment in the School of Materials Science and Engineering at the Georgia Institute of Technology. His research focuses on multiscale modelling of materials, microstructure-sensitive computational fatigue analysis of microstructures, methods for materials design that are robust against uncertainty, and coarse-grained atomistic modelling methods.