Discontinuous Galerkin reduced basis empirical quadrature procedure for model reduction of parametrized nonlinear conservation laws

We present a model reduction formulation for parametrized nonlinear partial differential equations (PDEs) associated with steady hyperbolic and convection-dominated conservation laws. Our formulation builds on three ingredients: a discontinuous Galerkin (DG) method which provides stability for conservation laws, reduced basis (RB) spaces which provide low-dimensional approximations of the parametric solution manifold, and the empirical quadrature procedure (EQP) which provides hyperreduction of the Galerkin-projection-based reduced model. The hyperreduced system inherits the stability of the DG discretization: (i) energy stability for linear hyperbolic systems, (ii) symmetry and non-negativity for steady linear diffusion systems, and hence (iii) energy stability for linear convection-diffusion systems. In addition, the framework provides (a) a direct quantitative control of the solution error induced by the hyperreduction, (b) efficient and simple hyperreduction posed as a l1 minimization problem, and (c) systematic identification of the reduced bases and the empirical quadrature rule by a greedy algorithm. We demonstrate the formulation for parametrized aerodynamics problems governed by the compressible Euler and Navier-Stokes equations.

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

[2]  A. Pinkus N-widths of Sobolev Spaces in L P , 2022 .

[3]  C. Farhat,et al.  Structure‐preserving, stability, and accuracy properties of the energy‐conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models , 2015 .

[4]  Paul T. Boggs,et al.  Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics , 2014, SIAM J. Sci. Comput..

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

[6]  N. Nguyen,et al.  EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS , 2007 .

[7]  Karen Willcox,et al.  Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics , 2003 .

[8]  F. Brezzi,et al.  Finite dimensional approximation of nonlinear problems , 1981 .

[9]  Bernhard Wieland,et al.  Reduced basis methods for partial differential equations with stochastic influences , 2013 .

[10]  J. Peraire,et al.  An efficient reduced‐order modeling approach for non‐linear parametrized partial differential equations , 2008 .

[11]  Theodore Kim,et al.  Optimizing cubature for efficient integration of subspace deformations , 2008, SIGGRAPH Asia '08.

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

[13]  Charbel Farhat,et al.  On the Use of Discrete Nonlinear Reduced-Order Models for the Prediction of Steady-State Flows Past Parametrically Deformed Complex Geometries , 2016 .

[14]  Matthew F. Barone,et al.  Stable Galerkin reduced order models for linearized compressible flow , 2009, J. Comput. Phys..

[15]  Lawrence Sirovich,et al.  Karhunen–Loève procedure for gappy data , 1995 .

[16]  Gerrit Welper,et al.  Interpolation of Functions with Parameter Dependent Jumps by Transformed Snapshots , 2017, SIAM J. Sci. Comput..

[17]  A. Ern,et al.  Mathematical Aspects of Discontinuous Galerkin Methods , 2011 .

[18]  Christian Rohde,et al.  An Introduction to Recent Developments in Theory and Numerics for Conservation Laws: Proceedings of the International School on Theory and Numerics for Conservation Laws, Freiburg/Littenweiler, Germany, October 20-24, 1997 , 1999, Theory and Numerics for Conservation Laws.

[19]  Mario Ohlberger,et al.  The method of freezing as a new tool for nonlinear reduced basis approximation of parameterized evolution equations , 2013, 1304.4513.

[20]  Stephen P. Boyd,et al.  Extensions of Gauss Quadrature Via Linear Programming , 2014, Found. Comput. Math..

[21]  J. Hesthaven,et al.  Nodal Discontinuous Galerkin Methods: Algorithms, Analysis, and Applications , 2007 .

[22]  Angelo Iollo,et al.  Advection modes by optimal mass transfer. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  David L. Darmofal,et al.  The Importance of Mesh Adaptation for Higher-Order Discretizations of Aerodynamic Flows , 2011 .

[24]  Bernardo Cockburn Discontinuous Galerkin methods , 2003 .

[25]  Anthony T. Patera,et al.  An LP empirical quadrature procedure for reduced basis treatment of parametrized nonlinear PDEs , 2019, Computer Methods in Applied Mechanics and Engineering.

[26]  A. Quarteroni,et al.  Reduced Basis Methods for Partial Differential Equations: An Introduction , 2015 .

[27]  Siep Weiland,et al.  Missing Point Estimation in Models Described by Proper Orthogonal Decomposition , 2004, IEEE Transactions on Automatic Control.

[28]  Mario Ohlberger,et al.  Nonlinear reduced basis approximation of parameterized evolution equations via the method of freezing , 2013 .

[29]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .

[30]  Béatrice Rivière,et al.  Discontinuous Galerkin methods for solving elliptic and parabolic equations - theory and implementation , 2008, Frontiers in applied mathematics.

[31]  S. Rebay,et al.  GMRES Discontinuous Galerkin Solution of the Compressible Navier-Stokes Equations , 2000 .

[32]  Anthony T. Patera,et al.  An LP empirical quadrature procedure for parametrized functions , 2017 .

[33]  Juan J. Alonso,et al.  Dynamic Domain Decomposition and Error Correction for Reduced Order Models , 2003 .

[34]  Charbel Farhat,et al.  Nonlinear Model Reduction for CFD Problems Using Local Reduced Order Bases , 2012 .

[35]  J. Hesthaven,et al.  Certified Reduced Basis Methods for Parametrized Partial Differential Equations , 2015 .

[36]  Stefan Görtz,et al.  Non-linear reduced order models for steady aerodynamics , 2010, ICCS.

[37]  Jacques Rappaz,et al.  Finite Dimensional Approximation of Non-Linear Problems .1. Branches of Nonsingular Solutions , 1980 .

[38]  Timothy J. Barth,et al.  Numerical Methods for Gasdynamic Systems on Unstructured Meshes , 1997, Theory and Numerics for Conservation Laws.

[39]  Juan J. Alonso,et al.  Investigation of non-linear projection for POD based reduced order models for Aerodynamics , 2001 .

[40]  M. Caicedo,et al.  Dimensional hyper-reduction of nonlinear finite element models via empirical cubature , 2017 .

[41]  Mario Ohlberger,et al.  Reduced Basis Methods: Success, Limitations and Future Challenges , 2015, 1511.02021.

[42]  Alfio Quarteroni,et al.  A discontinuous Galerkin reduced basis element method for elliptic problems , 2016 .

[43]  Mario Ohlberger,et al.  Error Control for the Localized Reduced Basis Multiscale Method with Adaptive On-Line Enrichment , 2015, SIAM J. Sci. Comput..

[44]  C. Farhat,et al.  Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy‐based mesh sampling and weighting for computational efficiency , 2014 .

[45]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .