Coupled Model and Grid Adaptivity in Hierarchical Reduction of Elliptic Problems

In this paper we propose a surrogate model for advection–diffusion–reaction problems characterized by a dominant direction in their dynamics. We resort to a hierarchical model reduction where we couple a modal representation of the transverse dynamics with a finite element approximation along the mainstream. This different treatment of the dynamics entails a surrogate model enhancing a purely 1D description related to the leading direction. The coefficients of the finite element expansion along this direction introduce a generally non-constant description of the transversal dynamics. Aim of this paper is to provide an automatic adaptive approach to locally select the dimension of the modal expansion as well as the finite element step in order to satisfy a prescribed tolerance on a goal functional of interest.

[1]  Mario Ohlberger,et al.  A new problem adapted hierarchical model reduction technique based on reduced basis methods and dimensional splitting , 2011 .

[2]  Anthony T. Patera,et al.  Domain Decomposition by the Mortar Element Method , 1993 .

[3]  Andrea Toselli,et al.  Domain decomposition methods : algorithms and theory , 2005 .

[4]  Rolf Rannacher,et al.  An optimal control approach to a posteriori error estimation in finite element methods , 2001, Acta Numerica.

[5]  Alexandre Ern,et al.  A Posteriori Control of Modeling Errors and Discretization Errors , 2003, Multiscale Model. Simul..

[6]  Pablo J. Blanco,et al.  Black-box decomposition approach for computational hemodynamics: One-dimensional models , 2011 .

[7]  Randolph E. Bank,et al.  A posteriori error estimates based on hierarchical bases , 1993 .

[8]  R. Bank,et al.  Some a posteriori error estimators for elliptic partial differential equations , 1985 .

[9]  Anne M. Robertson,et al.  A DIRECTOR THEORY APPROACH FOR MODELING BLOOD FLOW IN THE ARTERIAL SYSTEM: AN ALTERNATIVE TO CLASSICAL 1D MODELS , 2005 .

[10]  S. Perotto Adaptive modeling for free-surface flows , 2006 .

[11]  Alfio Quarteroni,et al.  Multiscale modelling of the circulatory system: a preliminary analysis , 1999 .

[12]  M. Giles,et al.  Adjoint methods for PDEs: a posteriori error analysis and postprocessing by duality , 2002, Acta Numerica.

[13]  Simona Perotto,et al.  Hierarchical Model Reduction for Advection-Diffusion-Reaction Problems , 2008 .

[14]  Simona Perotto,et al.  Hierarchical Local Model Reduction for Elliptic Problems: A Domain Decomposition Approach , 2010, Multiscale Model. Simul..

[15]  Yvon Maday,et al.  A reduced-basis element method , 2002 .

[16]  Mark Ainsworth,et al.  A posteriori error estimation for fully discrete hierarchic models of elliptic boundary value problems on thin domains , 1998, Numerische Mathematik.

[17]  J. Tinsley Oden,et al.  Estimation of local modeling error and goal-oriented adaptive modeling of heterogeneous materials: I. Error estimates and adaptive algorithms , 2000 .

[18]  J. Lions,et al.  Non-homogeneous boundary value problems and applications , 1972 .

[19]  Philippe G. Ciarlet,et al.  The finite element method for elliptic problems , 2002, Classics in applied mathematics.

[20]  C. Chui Wavelet Analysis and Its Applications , 1992 .

[21]  Barry Smith,et al.  Domain Decomposition Methods for Partial Differential Equations , 1997 .

[22]  Simona Perotto,et al.  Model Adaptation Enriched with an Anisotropic Mesh Spacing for Nonlinear Equations: Application to Environmental and CFD Problems , 2013 .

[23]  S. Ohnimus,et al.  Error-controlled adaptive goal-oriented modeling and finite element approximations in elasticity , 2007 .

[24]  Endre Süli,et al.  Adaptive finite element methods for differential equations , 2003, Lectures in mathematics.

[25]  Alfio Quarteroni,et al.  Cardiovascular mathematics : modeling and simulation of the circulatory system , 2009 .

[26]  E. Miglio,et al.  Model coupling techniques for free-surface flow problems: Part II , 2005 .

[27]  M. Hinze,et al.  Proper Orthogonal Decomposition Surrogate Models for Nonlinear Dynamical Systems: Error Estimates and Suboptimal Control , 2005 .

[28]  Almerico Murli,et al.  Numerical Mathematics and Advanced Applications , 2003 .

[29]  S.,et al.  " Goal-Oriented Error Estimation and Adaptivity for the Finite Element Method , 1999 .

[30]  Endre Süli,et al.  Poincaré-type inequalities for broken Sobolev spaces , 2003 .

[31]  J. Tinsley Oden,et al.  Estimation of modeling error in computational mechanics , 2002 .

[32]  Abdellatif Agouzal,et al.  Some remarks about the hierarchical a posteriori error estimate , 2004 .

[33]  Douglas N. Arnold,et al.  Unified Analysis of Discontinuous Galerkin Methods for Elliptic Problems , 2001, SIAM J. Numer. Anal..

[34]  Y. Maday,et al.  Two different approaches for matching nonconforming grids: The Mortar Element method and the Feti Method , 1997 .

[35]  Peter Benner,et al.  Dimension Reduction of Large-Scale Systems , 2005 .

[36]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[37]  P. Clément Approximation by finite element functions using local regularization , 1975 .

[38]  Ricardo H. Nochetto,et al.  Small data oscillation implies the saturation assumption , 2002, Numerische Mathematik.

[39]  Ivo Babuška,et al.  On a dimensional reduction method. III. A posteriori error estimation and an adaptive approach , 1981 .

[40]  Simona Perotto,et al.  Hierarchical Model (Hi-Mod) Reduction in Non-rectilinear Domains , 2014 .

[41]  Wolfgang Dahmen,et al.  Multiscale Wavelet Methods for Partial Differential Equations , 1997 .

[42]  J. Tinsley Oden,et al.  Modeling error and adaptivity in nonlinear continuum mechanics , 2001 .