Reduced Basis’ Acquisition by a Learning Process for Rapid On-line Approximation of Solution to PDE’s: Laminar Flow Past a Backstep

Reduced Basis’ Acquisition by a Learning Process for Rapid On-line Approximation of Solution to... Reduced basis methods for the approximation to parameter-dependent partial differential equations are now well-developed and start to be used for industrial applications. The classical implementation of the reduced basis method goes through two stages: in the first one, offline and time consuming, from standard approximation methods a reduced basis is constructed; then in a second stage, online and very cheap, a small problem, of the size of the reduced basis, is solved. The offline stage is a learning one from which the online stage can proceed efficiently. In this paper we propose to exploit machine learning procedures in both offline and online stages to either tackle different classes of problems or increase the speed-up during the online stage. The method is presented through a simple flow problem—a flow past a backward step governed by the Navier Stokes equations—which shows, however, interesting features. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Computational Methods in Engineering Springer Journals

Reduced Basis’ Acquisition by a Learning Process for Rapid On-line Approximation of Solution to PDE’s: Laminar Flow Past a Backstep

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by CIMNE, Barcelona, Spain
Subject
Engineering; Mathematical and Computational Engineering
ISSN
1134-3060
eISSN
1886-1784
D.O.I.
10.1007/s11831-017-9238-z
Publisher site
See Article on Publisher Site

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