1887

Abstract

Summary

Reduced order modeling techniques play an important role in many areas of science and engineering and are an emerging topic in geophysical simulations nowadays. We present pyROM, a comprehensive, user-friendly, and open-source computational framework for model order reduction in geophysical problems. Users have free access to the framework and can apply model order reduction methods to reproduce the dynamic response of the high-dimensional models with good accuracy while achieving significant computational savings. pyROM contains implementations of several efficient model reduction methods and is written in a clear and concise way targeting a wide range of users, including those with little or no knowledge of model reduction principles.

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/content/papers/10.3997/2214-4609.201801350
2018-06-11
2020-04-03
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References

  1. Akhtar, I., Nayfeh, A. and Ribbens, C.
    [2009] On the stability and extension of reduced-order Galerkin models in incompressible flows. Theoretical and Computational Fluid Dynamics, 23(3), 213–237.
    [Google Scholar]
  2. Belson, B.A., Tu, J.H. and Rowley, C.W.
    [2014] Algorithm 945: modred — a parallelized model reduction library. ACM Transactions on Mathematical Software (TOMS), 40(4), 30.
    [Google Scholar]
  3. Berkooz, G., Holmes, P. and Lumley, J.L.
    [1993] The proper orthogonal decomposition in the analysis of turbulent flows. Annual review of fluid mechanics, 25(1), 539–575.
    [Google Scholar]
  4. Chaturantabut, S. and Sorensen, D.C.
    [2010] Nonlinear model reduction via discrete empirical interpolation. SIAM Journal on Scientific Computing, 32(5), 2737–2764.
    [Google Scholar]
  5. Ghommem, M., Presho, M., Calo, V.M. and Efendiev, Y.
    [2013] Mode decomposition methods for flows in high-contrast porous media. Global—local approach. Journal of computational physics, 253, 226–238.
    [Google Scholar]
  6. Milk, R., Rave, S. and Schindler, F.
    [2016] pyMOR—Generic algorithms and interfaces for model order reduction. SIAM Journal on Scientific Computing, 38(5), S194–S216.
    [Google Scholar]
  7. Rowley, C.W.
    [2006] Model reduction for fluids, using balanced proper orthogonal decomposition. In: Modeling And Computations In Dynamical Systems, World Scientific, 301–317.
    [Google Scholar]
  8. Schmid, P.J.
    [2010] Dynamic mode decomposition of numerical and experimental data. Journal of fluid mechanics, 656, 5–28.
    [Google Scholar]
  9. Varga, A.
    [2001] Model reduction software in the SLICOT library. In: Applied and Computational Control, Signals, and Circuits, Springer, 239–282.
    [Google Scholar]
  10. Willcox, K. and Peraire, J.
    [2002] Balanced model reduction via the proper orthogonal decomposition. AIAA journal, 40(11), 2323–2330.
    [Google Scholar]
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