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Data-driven Modeling Using Uncertainty Quantification, Machine Learning and Optimization

James Stewart, Sandia National Laboratories

Roger Ghanem, University of Southern California

Miguel Bessa, TU Delft

Krisha Garikipati, University of Michigan

C. Alberto Figueroa, University of Michigan

Data-driven approaches are opening new avenues in computational mechanics and materials science. This minisymposium focuses on (1) recently developed methods for data-driven approaches, and (2) data-driven applications to fluids, structures and materials involving (but not limited to) machine learning, uncertainty quantification and/or optimization. Contributions addressing specific challenges relevant to this topic such as reduced order modeling and high-performance computing are also encouraged. Ideally, this minisymposium will reflect the generality of data-driven science and its broad applicability to the computational mechanics and materials science communities.