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?