Compressive sensing adaptation for polynomial chaos expansions

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present a new adaptation mechanism that builds on compressive sensing algorithms, resulting in a reduced polynomial chaos approximation with optimal sparsity. The developed adaptation algorithm consists of a two-step optimization procedure that computes the optimal coefficients and the input projection matrix of a low dimensional chaos expansion with respect to an optimally rotated basis. We demonstrate the attractive features of our algorithm through several numerical examples including the application on Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE scramjet engine. (C) 2018 Elsevier Inc. All rights reserved.


Published in:
Journal Of Computational Physics, 380, 29-47
Year:
Mar 01 2019
Publisher:
San Diego, ACADEMIC PRESS INC ELSEVIER SCIENCE
ISSN:
0021-9991
1090-2716
Keywords:
Laboratories:




 Record created 2019-06-18, last modified 2019-06-26


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