Tsilifis, PanagiotisHuan, XunSafta, CosminSargsyan, KhachikLacaze, GuilhemOefelein, Joseph C.Najm, Habib N.Ghanem, Roger G.2019-06-182019-06-182019-06-182019-03-0110.1016/j.jcp.2018.12.010https://infoscience.epfl.ch/handle/20.500.14299/157849WOS:000458145900002Basis 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.Computer Science, Interdisciplinary ApplicationsPhysics, MathematicalComputer SciencePhysicspolynomial chaosbasis adaptationcompressive sensingl(1)-minimizationdimensionality reductionuncertainty propagationuncertainty quantificationflow simulationsoptimizationpropagationreductionsystemstext::journal::journal article::research article