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Data-driven reduced order modeling for time-dependent problems
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DOI：10.1016/j.cma.2018.10.029.png)
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Abstract
En 中文
A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and the projection coefficients onto the RB are approximated as a regression model. With a natural tensor grid between the time and the parameters in the database, a singular-value decomposition (SVD) is used to extract the principal components in the data of projection coefficients. The regression functions are represented as the linear combinations of several tensor products of two Gaussian processes, one of time and the other of parameters. During the online stage, the solutions at new time/parameter locations in the domain of interest can be recovered rapidly as outputs from the regression models. Featuring a non-intrusive nature and the complete decoupling of the offline and online stages, the proposed approach provides a reliable and efficient tool for approximating parametrized time-dependent problems, and its effectiveness is illustrated by non-trivial numerical examples. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Data-driven
Non-intrusive reduced order modeling
Time-dependent problem
Low-rank approximation
Gaussian process regression
Machine learning
Journal
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7.3
Papers:
1.3W
Citations:
5.6W
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