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math.NA2023
Data augmentation for the POD formulation of the parametric laminar incompressible Navier-Stokes equations
Alba Muixí, Sergio Zlotnik, Matteo Giacomini +1
A posteriori reduced-order models (ROM), e.g. based on proper orthogonal decomposition (POD), are essential to affordably tackle realistic parametric problems. They rely on a trust…
math.NA2021
Nonlinear dimensionality reduction for parametric problems: a kernel Proper Orthogonal Decomposition (kPOD)
Pedro Díez, Alba Muixí, Sergio Zlotnik +1
Reduced-order models are essential tools to deal with parametric problems in the context of optimization, uncertainty quantification, or control and inverse problems. The set of pa…