08 June 2022    11:00 - 13:00   Add
Multi-fidelity methods for uncertainty quantification and optimization I
Minisymposium organized by Lorenzo Tamellini, Matteo Diez, John Jakeman and Alex Gorodetsky
Room: Spitsbergen
Chair: Lorenzo Tamellini
Advanced Experiments on Gaussian Process-based Multi-fidelity Methods over Diverse Mathematical Characteristics
Sihmehmet Yildiz, Hayriye Pehlivan-Solak, Matteo Diez, Omer Goren and Melike Nikbay

Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models for Uncertainty Quantification
Markus P. Rumpfkeil and Phil Beran

Multifidelity ductile failure model by cokriging between simulations on unit cells and random microstructures
Clément Cadet, Jacques Besson, Sylvain Flouriot, Samuel Forest, Pierre Kerfriden, Laurent Lacourt and Victor de Rancourt

Domain-aware multifidelity learning for design optimization
Francesco Di Fiore and Laura Mainini

Multi-fidelity active learning for shape optimization problems affected by noise
Jeroen Wackers, Riccardo Pellegrini, Matteo Diez, Andrea Serani and Michel Visonneau

Comparing two multi-fidelity methods for forward uncertainty quantification of ship resistance
Chiara Piazzola , Lorenzo Tamellini, Riccardo Pellegrini, Riccardo Broglia, Andrea Serani and Matteo Diez