A local multiple proper generalized decomposition based on the partition of unity

It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.

[1]  J. Fish The s-version of the finite element method , 1992 .

[2]  C. Farhat,et al.  Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity , 2008 .

[3]  Gianluigi Rozza,et al.  Certified reduced basis approximation for parametrized partial differential equations and applications , 2011 .

[4]  Icíar Alfaro,et al.  Local proper generalized decomposition , 2017 .

[5]  Adrien Leygue,et al.  An overview of the proper generalized decomposition with applications in computational rheology , 2011 .

[6]  J. Hesthaven,et al.  Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Partial Differential Equations , 2007 .

[7]  I. Babuska,et al.  The partition of unity finite element method , 1996 .

[8]  I. Babuska,et al.  The Partition of Unity Method , 1997 .

[9]  P. Ladevèze,et al.  The LATIN multiscale computational method and the Proper Generalized Decomposition , 2010 .

[10]  Francisco Chinesta,et al.  Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models , 2010 .

[11]  H. Park,et al.  The use of the Karhunen-Loève decomposition for the modeling of distributed parameter systems , 1996 .

[12]  Francisco Chinesta,et al.  On the deterministic solution of multidimensional parametric models using the Proper Generalized Decomposition , 2010, Math. Comput. Simul..

[13]  Elías Cueto,et al.  PGD-Based Modeling of Materials, Structures and Processes , 2014 .

[14]  Kari Karhunen,et al.  Über lineare Methoden in der Wahrscheinlichkeitsrechnung , 1947 .

[15]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[16]  Elías Cueto,et al.  Coupling finite elements and proper generalized decompositions , 2011 .

[17]  David González,et al.  Real‐time direct integration of reduced solid dynamics equations , 2014 .

[18]  Abubakr Gafar Abdalla,et al.  Probability Theory , 2017, Encyclopedia of GIS.

[19]  Francisco Chinesta,et al.  Computational vademecums for real‐time simulation of surgical cutting in haptic environments , 2016 .

[20]  David González,et al.  Computational Patient Avatars for Surgery Planning , 2015, Annals of Biomedical Engineering.

[21]  Icíar Alfaro,et al.  Towards a pancreatic surgery simulator based on model order reduction , 2015, Adv. Model. Simul. Eng. Sci..

[22]  I. Babuska,et al.  The partition of unity finite element method: Basic theory and applications , 1996 .

[23]  Y. Maday,et al.  Results and Questions on a Nonlinear Approximation Approach for Solving High-dimensional Partial Differential Equations , 2008, 0811.0474.

[24]  Siamak Niroomandi,et al.  Model order reduction for hyperelastic materials , 2010 .

[25]  Adrien Leygue,et al.  The Proper Generalized Decomposition for Advanced Numerical Simulations: A Primer , 2013 .

[26]  Gianluigi Rozza,et al.  Fundamentals of reduced basis method for problems governed by parametrized PDEs and applications , 2014 .

[27]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[28]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[29]  E. Rank,et al.  A multiscale finite-element method , 1997 .

[30]  Marcus Meyer,et al.  Efficient model reduction in non-linear dynamics using the Karhunen-Loève expansion and dual-weighted-residual methods , 2003 .

[31]  Pierre Ladevèze,et al.  Separated Representations and PGD-Based Model Reduction , 2014 .

[32]  Francisco Chinesta,et al.  A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids , 2006 .