1801 Data-driven Modeling Using Uncertainty Quantification, Machine Learning and Optimization

Miguel Bessa, Delft University of Technology
Julien Yvonnet, Université Paris-Est
Krishna Garikipati, University of Michigan
James Stewart, Sandia National Laboratories
Roger Ghanem, University of Southern California
Alberto Figueroa, University of Michigan
Karthik Duraisamy, 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.