Coupling finite elements and reduced approximation bases
DOI:
https://doi.org/10.13052/EJCM.18.445-463Keywords:
model reduction, proper orthogonal decomposition, separated representations, finite elementsAbstract
Models encountered in computational physics and engineering, usually involve too many degrees of freedom, too many simulation time-steps, too many iterations (e.g. non-linear models, optimization or inverse identification…), or simply excessive simulation time (for example when simulation in real time is envisaged). In some of our former works different reduction techniques were developed, some of them based on the use of an adaptive proper orthogonal decomposition and the other ones based on the use of separated representations. In this paper we are analyzing the coupling between reduced basis and standard finite element descriptions.
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Ammar A., Ryckelynck R., Chinesta F., Keunings R., “On the reduction of kinetic theory
models related to finitely extensible dumbbells”, Journal of Non-Newtonian Fluid
Mechanics, 134, 2006, p. 136-147.
Ammar A., Mokdad B., Chinesta F., Keunings R., “A new family of solvers for some classes
of multidimensional partial differential equations encountered in kinetic theory modeling
of complex fluids”, Journal of Non-Newtonian Fluid Mechanics, 139, 2006, p. 153-176.
Ammar A., Mokdad B., Chinesta F., Keunings R., “A new family of solvers for some classes
of multidimensional partial differential equations encountered in kinetic theory modeling
of complex fluids. Part II: Transient simulation using space-time separated
representation”, Journal of Non-Newtonian Fluid Mechanics, 144, 2007, p. 98-121.
Chinesta F., Ammar A., Lemarchand F., Beauchene P., Boust F., “Alleviating mesh
constraints: Model reduction, parallel time integration and high resolution
homogenization”, Computer Methods in Applied Mechanics and Engineering, 197/5,
, p. 400-413.
Holmes P.J., Lumleyc J.L., Berkoozld G., Mattinglya J.C., Wittenberg R.W., “Lowdimensional
models of coherent structures in turbulence”, Physics Reports, 287, 1997.
Karhunen K., “Uber lineare methoden in der wahrscheinlichkeitsrechnung”, Ann. Acad. Sci.
Fennicae, ser. Al. Math. Phys., 37, 1946.
Krysl P., Lall S., Marsden J.E., “Dimensional model reduction in non-linear finite element
dynamics of solids and structures”, International Journal Numerical Methods in
Engineering, 51, 2001, p. 479-504.
Ladeveze P., Nonlinear computational structural mechanics, Springer, NY, 1999.
Loève M.M., “Probability theory”, The University Series in Higher Mathematics, 3rd Ed. Van
Nostrand, Princeton, NJ, 1963.
Lorenz E.N., Empirical orthogonal functions and statistical weather prediction. MIT,
Departement of Meteorology, Scientific Report N1, Statistical Forecasting Project, 1956.
A. Nouy, “Recent developments in spectral stochastic methods for the solution of stochastic
partial differential equations”, Archives of Computational Methods in Engineering,
vol. 16, n° 3, 2009, p. 251-285.
Park H.M., Cho D.H., “The Use of the Karhunen-Loève decomposition for the modelling of
distributed parameter systems”, Chemical Engineering Science, 51, p. 81-98, 1996.
Ryckelynck D., “A Priori Hyperreduction Method: an adaptive approach”, Journal of
Computational Physics, 202, 2005, p. 346-366.
Ryckelynck D., Chinesta F., Cueto E., Ammar A., “On the ‘a priori’ model reduction:
Overview and recent developments”, Archives of Computational Methods in Engineering,
State of the Art Reviews, 13/1, 2006, p. 91-128.
Sirovich L., “Turbulence and the dynamics of coherent structures part I: Coherent structures”,
Quaterly of applied mathematics, XLV, 1987, p. 561-571.