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Algorithm 945: modred—A Parallelized Model Reduction Library

Published: 08 July 2014 Publication History

Abstract

We describe a new parallelized Python library for model reduction, modal analysis, and system identification of large systems and datasets. Our library, called modred, handles a wide range of problems and any data format.
The modred library contains implementations of the Proper Orthogonal Decomposition (POD), balanced POD (BPOD) Petrov-Galerkin projection, and a more efficient variant of the Dynamic Mode Decomposition (DMD). The library contains two implementations of these algorithms, each with its own advantages. One is for smaller and simpler datasets, requires minimal knowledge to use, and follows a common matrix-based formulation. The second, for larger and more complicated datasets, preserves the abstraction of vectors as elements of a vector space and, as a result, allows the library to work with arbitrary data formats and eases distributed memory parallelization. We also include implementations of the Eigensystem Realization Algorithm (ERA), and Observer/Kalman Filter Identification (OKID). These methods are typically not computationally demanding and are not parallelized. The library is designed to be easy to use, with an object-oriented design, and includes comprehensive automated tests. In almost all cases, parallelization is done internally so that scripts that use the parallelized classes can be run in serial or in parallel without any modifications.

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Software for modred - A Parallelized Model Reduction Library

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Published In

cover image ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software  Volume 40, Issue 4
June 2014
154 pages
ISSN:0098-3500
EISSN:1557-7295
DOI:10.1145/2639949
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 08 July 2014
Accepted: 01 November 2013
Revised: 01 October 2013
Received: 01 June 2013
Published in TOMS Volume 40, Issue 4

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Author Tags

  1. Balanced Proper Orthogonal Decomposition
  2. Dynamic Mode Decomposition
  3. Eigensystem Realization Algorithm
  4. Koopman modes
  5. Model reduction
  6. Observer/Kalman Filter Identification
  7. Proper Orthogonal Decomposition
  8. Python
  9. system identification

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  • (2023) Three-dimensional flow structures in turbulent Rayleigh–Bénard convection at low Prandtl number Pr  = 0.03 Journal of Fluid Mechanics10.1017/jfm.2023.794974Online publication date: 9-Nov-2023
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