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Comparing sampling strategies for aerodynamic Kriging surrogate models
Author(s) -
Rosenbaum B.,
Schulz V.
Publication year - 2012
Publication title -
zamm ‐ journal of applied mathematics and mechanics / zeitschrift für angewandte mathematik und mechanik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 51
eISSN - 1521-4001
pISSN - 0044-2267
DOI - 10.1002/zamm.201100112
Subject(s) - kriging , surrogate model , solver , interpolation (computer graphics) , sampling (signal processing) , computer science , aerodynamics , computation , range (aeronautics) , adaptive sampling , mathematical optimization , set (abstract data type) , algorithm , machine learning , mathematics , statistics , artificial intelligence , aerospace engineering , engineering , monte carlo method , motion (physics) , filter (signal processing) , computer vision , programming language
In aerodynamic applications often evaluations of an expensive computer simulation like a CFD solver are needed for a whole range of input parameters. Dense computations to describe the global behavior of an objective function are out of reach due to limited computational resources. Surrogate models like the Kriging method allow an interpolation of collected data and a global approximation. Adaptive sampling strategies can reduce the number of required samples for accurate and efficient surrogate models by automatically identifying critical or too coarse sampled regions of the input domain. We compare different existing sampling strategies as well as new theoretical methods using a dense set of validation data in order to gain a deeper understanding of optimal sample distributions and lower error boundaries.