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.