Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

DOI10.1007/978-3-319-58786-8_16zbMath1468.76052OpenAlexW2605547890MaRDI QIDQ4637164

Thomas Franz, Stefan Görtz, Ralf Zimmermann

Publication date: 18 April 2018

Published in: Model Reduction of Parametrized Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-58786-8_16

zbMATH Keywords

Navier-Stokes equationstransonic flowadaptive refinementlow-dimensional embedding spacenon-Euclidean Isomap metric

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Transonic flows (76H05) Basic methods in fluid mechanics (76M99) Compressible Navier-Stokes equations (76N06)

Uses Software

• Spalart-Allmaras

Cites Work

• On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
• Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
• Optimality of the Delaunay triangulation in \(\mathbb{R}^ d\)
• Interpolation-based reduced-order modelling for steady transonic flows via manifold learning
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