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Issue No.02 - Mar.-Apr. (2014 vol.16)
pp: 44-53
Khushbu Agarwal , Pacific Northwest National Laboratory
Poorva Sharma , Pacific Northwest National Laboratory
Jinliang Ma , National Energy Technology Laboratory
Chaomei Lo , Pacific Northwest National Laboratory
Ian Gorton , Pacific Northwest National Laboratory
Yan Liu , Concordia University
ABSTRACT
Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations known as reduced-order models (ROMs). This article presents the design and implementation of the Reveal toolset, a ROM builder that generates ROMs based on science- and engineering-domain-specific simulations executed on high-performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods for ROM generation, automatically quantifies ROM accuracy, and supports an iterative approach to improve ROM accuracy. Reveal is designed to be extensible for any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so users can mix and match mathematical techniques best suited to their model characteristics. The article describes the architecture of Reveal and demonstrates its use with a computational fluid dynamics model used in carbon capture.
INDEX TERMS
Computational modeling, Read only memory, Analytical models, Data models, Simulation, Mathematical model, Reduced order systems,scientific computing, reduced-order model, simulation, modeling, surrogate model, reusable software
CITATION
Khushbu Agarwal, Poorva Sharma, Jinliang Ma, Chaomei Lo, Ian Gorton, Yan Liu, "Reveal: An Extensible Reduced-Order Model Builder for Simulation and Modeling", Computing in Science & Engineering, vol.16, no. 2, pp. 44-53, Mar.-Apr. 2014, doi:10.1109/MCSE.2013.46
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