Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

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We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions. The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator: We exploit the non-Euclidean Isomap metric to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.
SprogEngelsk
TitelModel Reduction of Parametrized Systems
RedaktørerPeter Brenner, Mario Ohlberger, Anthony Patera, Gianluigi Rozza, Karsten Urban
ForlagSpringer
Publikationsdato2017
Sider225-269
Kapitel16
ISBN (Trykt)978-3-319-58785-1
ISBN (Elektronisk)978-3-319-58786-8
DOI
StatusUdgivet - 2017
BegivenhedModel Reduction of Parametrized Systems III - SISSA, Trieste, Italien
Varighed: 13 okt. 201516 okt. 2015
http://www.sissa.it/morepas2015

Konference

KonferenceModel Reduction of Parametrized Systems III
LokationSISSA
LandItalien
ByTrieste
Periode13/10/201516/10/2015
Internetadresse
NavnModeling, Simulation & Applications
Vol/bind17
ISSN2037-5255

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