A hybrid alternating least squares - TT-cross algorithm for parametric PDEs

• Verfasst von: Dolgov, Sergey [VerfasserIn]
• Verfasst von: Scheichl, Robert [VerfasserIn]
• Titel: A hybrid alternating least squares - TT-cross algorithm for parametric PDEs
• Verf.angabe: Sergey Dolgov and Robert Scheichl
• E-Jahr: 2019
• Jahr: [2019]
• Umfang: 32 S.
• Fussnoten: Gesehen am 24.06.2019
• Titel Quelle: Enthalten in: Society for Industrial and Applied MathematicsSIAM ASA journal on uncertainty quantification
• Ort Quelle: Philadelphia, Pa. : SIAM, 2013
• Jahr Quelle: 2019
• Band/Heft Quelle: 7(2019), 1, Seite 260-291
• ISSN Quelle: 2166-2525
• DOI: doi:10.1137/17M1138881
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1667812343

Abstract

We consider the approximate solution of parametric PDEs using the low-rank tensor train (TT) decomposition. Such parametric PDEs arise, for example, in uncertainty quantification problems in engineering applications. We propose an algorithm that is a hybrid of the alternating least squares and the TT-cross methods. It computes a TT approximation of the whole solution, which is beneficial when multiple quantities of interest are sought. This might be needed, for example, for the computation of the probability density function via the maximum entropy method [M. Kavehrad and M. Joseph, IEEE Trans. Comm., 34 (1986), pp. 1183--1189]. The new algorithm exploits and preserves the block-diagonal structure of the discretized operator in stochastic collocation schemes. This disentangles computations of the spatial and parametric degrees of freedom in the TT representation. In particular, it only requires solving independent PDEs at a few parameter values, thus allowing the use of existing high performance PDE solvers. In our numerical experiments, we apply the new algorithm to the stochastic diffusion equation and compare it with preconditioned steepest descent in the TT format, as well as with (multilevel) quasi--Monte Carlo and dimension-adaptive sparse grids methods. For sufficiently smooth random fields the new approach is orders of magnitude faster.