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

Alex Gorodetsky, University of Michigan

Gianluca Geraci, Sandia National Labroratories

John Jakeman, Sandia National Laboratories

This minisymposium will present the latest advancements in multi-level and multi-fidelity algorithms for inference and parameter estimation, uncertainty propagation, experimental design, and data-driven learning. Questions and topics that are particularly of particular interest for the minisymposium are: (1) what information can be efficiently used as multi-fidelity models across different application areas, (2) how can advancements in function representation and training be leveraged for the multi-fidelity enterprise, (3) how can multi-fidelity tools be leveraged in challenging unsteady, nonlinear, and/or chaotic regimes? Presentations that focus on either or both algorithmic developments and applications that demonstrate the advantages of this rapidly emerging set of tools are welcomed.