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Data-Enhanced Modeling and Uncertainty Quantification of Systems with Multiple Fidelities

Alex Gorodetsky, University of Michigan
John Jakeman, Sandia National Laboratories
Mike Eldred, Sandia National Laboratories
Gianluca Geraci, Sandia National Laboratories
This minisymposium will present the latest advancements in multi-level and multi-fidelity algorithms for learning and uncertainty quantification. Talks will address one, or multiple, aspects of the development and/or deployment of advanced multi-fidelity tools, spanning inference and estimation, uncertainty propagation, experimental design, and data-driven learning. Topics and questions of high-interest include, but are not limited to: (1) what constitutes an effective multi-fidelity model ensemble? (2) how can model ensembles be adaptively tuned or developed to improve the performance of multi-fidelity algorithms? (3) how can structure be identified and exploited to improve multi-fidelity analysis, (4) what are relationships between multi-fidelity modeling, multi-task learning, and transfer learning,
and how can they be exploited? and (5) how can multi-fidelity tools be leveraged in challenging unsteady, nonlinear, and/or chaotic regimes?