Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations
A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling its processing in real time. The right reduced‐order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposit...
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| Vydáno v: | International journal for numerical methods in engineering Ročník 86; číslo 2; s. 155 - 181 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Chichester, UK
John Wiley & Sons, Ltd
15.04.2011
Wiley |
| Témata: | |
| ISSN: | 0029-5981, 1097-0207, 1097-0207 |
| On-line přístup: | Získat plný text |
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| Abstract | A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling its processing in real time. The right reduced‐order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposition method. The left reduced‐order basis is selected to minimize the two‐norm of the residual arising at each Newton iteration. Thus, this basis is iteration‐dependent, enables capturing of non‐linearities, and leads to the globally convergent Gauss–Newton method. To avoid the significant computational cost of assembling the reduced‐order operators, the residual and action of the Jacobian on the right reduced‐order basis are each approximated by the product of an invariant, large‐scale matrix, and an iteration‐dependent, smaller one. The invariant matrix is computed using a data compression procedure that meets proposed consistency requirements. The iteration‐dependent matrix is computed to enable the least‐squares reconstruction of some entries of the approximated quantities. The results obtained for the solution of a turbulent flow problem and several non‐linear structural dynamics problems highlight the merit of the proposed consistency requirements. They also demonstrate the potential of this method to significantly reduce the computational cost associated with high‐dimensional non‐linear models while retaining their accuracy. Copyright © 2010 John Wiley & Sons, Ltd. |
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| AbstractList | A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling its processing in real time. The right reduced‐order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposition method. The left reduced‐order basis is selected to minimize the two‐norm of the residual arising at each Newton iteration. Thus, this basis is iteration‐dependent, enables capturing of non‐linearities, and leads to the globally convergent Gauss–Newton method. To avoid the significant computational cost of assembling the reduced‐order operators, the residual and action of the Jacobian on the right reduced‐order basis are each approximated by the product of an invariant, large‐scale matrix, and an iteration‐dependent, smaller one. The invariant matrix is computed using a data compression procedure that meets proposed consistency requirements. The iteration‐dependent matrix is computed to enable the least‐squares reconstruction of some entries of the approximated quantities. The results obtained for the solution of a turbulent flow problem and several non‐linear structural dynamics problems highlight the merit of the proposed consistency requirements. They also demonstrate the potential of this method to significantly reduce the computational cost associated with high‐dimensional non‐linear models while retaining their accuracy. Copyright © 2010 John Wiley & Sons, Ltd. A Petrov-Galerkin projection method is proposed for reducing the dimension of a discrete non-linear static or dynamic computational model in view of enabling its processing in real time. The right reduced-order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposition method. The left reduced-order basis is selected to minimize the two-norm of the residual arising at each Newton iteration. Thus, this basis is iteration-dependent, enables capturing of non-linearities, and leads to the globally convergent Gauss-Newton method. To avoid the significant computational cost of assembling the reduced-order operators, the residual and action of the Jacobian on the right reduced-order basis are each approximated by the product of an invariant, large-scale matrix, and an iteration-dependent, smaller one. The invariant matrix is computed using a data compression procedure that meets proposed consistency requirements. The iteration-dependent matrix is computed to enable the least-squares reconstruction of some entries of the approximated quantities. The results obtained for the solution of a turbulent flow problem and several non-linear structural dynamics problems highlight the merit of the proposed consistency requirements. They also demonstrate the potential of this method to significantly reduce the computational cost associated with high-dimensional non-linear models while retaining their accuracy. Copyright 2010 John Wiley & Sons, Ltd. |
| Author | Farhat, Charbel Bou-Mosleh, Charbel Carlberg, Kevin |
| Author_xml | – sequence: 1 givenname: Kevin surname: Carlberg fullname: Carlberg, Kevin email: carlberg@stanford.edu organization: Department of Aeronautics and Astronautics, Stanford University, Mail Code 3035, Stanford, CA 94305, U.S.A – sequence: 2 givenname: Charbel surname: Bou-Mosleh fullname: Bou-Mosleh, Charbel organization: Department of Mechanical Engineering, Notre Dame University, P.O. Box 72 Zouk Mikael, Louaize, Lebanon – sequence: 3 givenname: Charbel surname: Farhat fullname: Farhat, Charbel organization: Department of Aeronautics and Astronautics, Stanford University, Mail Code 3035, Stanford, CA 94305, U.S.A |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23932215$$DView record in Pascal Francis |
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| CODEN | IJNMBH |
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| Cites_doi | 10.1016/j.cma.2005.08.026 10.2514/6.1999-655 10.1137/1.9780898718713 10.1017/S0022112004001338 10.1016/j.paerosci.2005.02.001 10.1016/j.compfluid.2004.11.006 10.2514/1.11246 10.1016/j.crma.2004.08.006 10.1364/JOSAA.12.001657 10.23919/ACC.2004.1384488 10.2514/6.2003-3847 10.2514/2.867 10.1007/s11071-005-2803-2 10.2514/2.1570 10.1177/1077546302008001518 10.2514/2.3128 10.1109/TAC.2008.2006102 10.1002/nme.2309 10.1137/070694855 10.1016/S0889-9746(03)00044-6 10.1016/j.jfluidstructs.2003.06.002 10.2514/6.2003-4213 10.1137/1.9781611970944 10.2514/1.24512 10.1137/1.9780898718003 10.2514/1.35374 10.1090/qam/910462 10.1007/BF03024948 10.1051/m2an:2007031 10.1002/fld.867 10.2514/6.2000-2545 10.1063/1.2033624 10.2514/6.2006-1819 10.2514/1.2159 10.1017/CBO9780511622700 10.1002/nme.2746 10.1137/S0036142901389049 10.1016/j.physd.2003.03.001 |
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| Keywords | Invariant Turbulent flow Karhunen Loeve transformation Vibration compressive approximation Gauss Newton method Real time Modeling gappy data Galerkin-Petrov method discrete non-linear systems Static model Petrov―Galerkin projection Least squares method Galerkin method Reduced order systems proper orthogonal decomposition Non linear effect Incomplete information Dynamic model non-linear model reduction |
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| References | Everson R, Sirovich L. Karhunen-Loeve procedure for gappy data. Journal of the Optical Society of America A 1995; 12(8):1657-1664. Bui-Thanh T, Willcox K, Ghattas O. Model reduction for large-scale systems with high-dimensional parametric input space. SIAM Journal on Scientific Computing 2008; 30(6):3270-3288. Queipo N, Haftka R, Shyy W, Goel T, Vaidyanathan R, Tucker P. Surrogate-based analysis and optimization. Progress in Aerospace Sciences 2005; 41(1):1-28. Kerschen G, Golinval J-C, Vakakis AF, Bergman LA. The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview. Nonlinear Dynamics 2005; 41:147-169. Golub GH, Loan CFV. Matrix Computations (3rd edn). Johns Hopkins University Press: Baltimore, MD, 1996. Lieu T, Farhat C, Lesoinne M. Reduced-order fluid/structure modeling of a complete aircraft configuration. Computer Methods in Applied Mechanics and Engineering 2006; 195:5730-5742. Bergmann M, Cordier L, Brancher J-P. Optimal rotary control of the cylinder wake using proper orthogonal decomposition reduced-order model. Physics of Fluids 2005; 17(9):80-101. Barrault M, Maday Y, Nguyen N, Patera A. An 'empirical interpolation' method: application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus Mathématique Académie des Sciences 2004; 339(9):667-672. Sirovich L. Turbulence and the dynamics of coherent structures. I-coherent structures. Quarterly of Applied Mathematics 1987; 45:561-571. Epureanu BI. A parametric analysis of reduced order models of viscous flows in turbomachinery. Journal of Fluids and Structures 2003; 17:971-982. Veroy K, Patera AT. Certified real-time solution of the parametrized steady incompressible Navier-Stokes equations: rigorous reduced-basis a posteriori error bounds. International Journal for Numerical Methods in Fluids 2005; 47(8):773-788. Grepl MA, Maday Y, Nguyen NC, Patera AT. Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations. Mathematical Modelling and Numerical Analysis 2007; 41(3):575-605. Galbally D, Fidkowski K, Willcox K, Ghattas O. Non-linear model reduction for uncertainty quantification in large-scale inverse problems. International Journal for Numerical Methods in Engineering 2010; 81(12):1581-1608. Amabili M, Sarkar A, Paidoussis MP. Reduced-order models for nonlinear vibrations of cylindrical shells via the proper orthogonal decomposition method. Journal of Fluids and Structures 2003; 18(2):227-250. Rathinam M, Petzold LR. A new look at proper orthogonal decomposition. SIAM Journal on Numerical Analysis 2003; 41(5):1893-1925. Venturi D, Karniadakis G. Gappy data and reconstruction procedures for flow past a cylinder. Journal of Fluid Mechanics 2004; 519:315-336. Amsallem D, Farhat C. An interpolation method for adapting reduced-order models and application to aeroelasticity. AIAA Journal 2008; 46:1803-1813. Astrid P, Weiland S, Willcox K, Backx T. Missing point estimation in models described by proper orthogonal decomposition. IEEE Transactions on Automatic Control 2008; 53(10):2237-2251. Nocedal J, Wright SJ. Numerical Optimization (2nd edn). Springer: Berlin, 2006. Thomas JP, Dowell EH, Hall KC. Three-dimensional transonic aeroelasticity using proper orthogonal decomposition-based reduced order models. Journal of Aircraft 2003; 40(3):544-551. Kim T, Hong M, Bhatia KB, SenGupta G. Aeroelastic model reduction for affordable computational fluid dynamics-based flutter analysis. AIAA Journal 2005; 43(12):2487-2495. Willcox K. Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition. Computers and Fluids 2006; 35(2):208-226. Han S, Feeny BF. Enhanced proper orthogonal decomposition for the modal analysis of homogeneous structures. Journal of Vibration and Control 2002; 8(1):19-40. Amsallem D, Carlberg K, Cortial J, Farhat C. A method for interpolating on manifolds structural dynamics reduced-order models. International Journal for Numerical Methods in Engineering 2009; 78:275-295. Rozza G, Huynh DBP, Patera AT. Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Archives of Computational Methods in Engineering 2007; 15(3):1-47. Bui-Thanh T, Damodaran M, Willcox K. Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. AIAA Journal 2004; 42(8):1505-1516. Willcox K, Peraire J. Balanced model reduction via the proper orthogonal decomposition. AIAA Journal 2002; 40(11):2323-2330. Nguyen NC, Peraire J. An efficient reduced-order modeling approach for non-linear parametrized partial differential equations. International Journal for Numerical Methods in Engineering 2008; 76:27-55. Saad Y. Iterative Methods for Sparse Linear Systems (2nd edn). Society for Industrial and Applied Mathematics: Philadelphia, PA, 2003. Rowley C, Colonius T, Murray R. Model reduction for compressible flows using POD and Galerkin projection. Physica D: Nonlinear Phenomena 2004; 189(1-2):115-129. Hall KC, Thomas JP, Dowell EH. Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows. AIAA Journal 2000; 38(2):1853-1862. Kelley CT. Iterative Methods for Linear and Nonlinear Equations. Society for Industrial and Applied Mathematics: Philadelphia, PA, 1995. Antoulas AC. Approximation of Large-scale Dynamical Systems. Society for Industrial and Applied Mathematics: Philadelphia, PA, 2005. Holmes P, Lumley J, Berkooz G. Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge University Press: Cambridge, 1996. Lieu T, Farhat C. Adaptation of aeroelastic reduced-order models and application to an F-16 configuration. AIAA Journal 2007; 45:1244-1269. 2004; 189 2004; 42 2006; 35 1995; 12 2002; 8 2009 2006; 195 2005; 41 1996 2007 2005; 43 2006 1995 2005 2003; 17 2008; 76 2003; 18 2004 2003 2008; 30 2008; 53 2010; 81 2007; 15 2005; 47 2009; 78 1987; 45 2000; 38 2004; 19 2002; 40 2008; 46 1984 2007; 41 2004; 339 2003; 40 2005; 17 2007; 45 2003; 41 2004; 519 Nocedal J (e_1_2_13_37_2) 2006 e_1_2_13_47_2 e_1_2_13_26_2 e_1_2_13_45_2 e_1_2_13_25_2 e_1_2_13_46_2 e_1_2_13_20_2 e_1_2_13_43_2 e_1_2_13_44_2 e_1_2_13_22_2 e_1_2_13_21_2 e_1_2_13_42_2 e_1_2_13_8_2 e_1_2_13_6_2 e_1_2_13_5_2 e_1_2_13_9_2 Amsallem D (e_1_2_13_23_2) 2009; 78 e_1_2_13_16_2 e_1_2_13_39_2 e_1_2_13_17_2 e_1_2_13_38_2 e_1_2_13_18_2 e_1_2_13_19_2 Ahmed SR (e_1_2_13_40_2) 1984 e_1_2_13_12_2 e_1_2_13_35_2 e_1_2_13_13_2 e_1_2_13_34_2 e_1_2_13_14_2 e_1_2_13_15_2 e_1_2_13_31_2 e_1_2_13_30_2 e_1_2_13_10_2 e_1_2_13_33_2 e_1_2_13_11_2 e_1_2_13_32_2 Golub GH (e_1_2_13_36_2) 1996 Grepl MA (e_1_2_13_24_2) 2007 Nguyen NC (e_1_2_13_7_2) 2005 e_1_2_13_4_2 e_1_2_13_3_2 e_1_2_13_2_2 Hinterberger C (e_1_2_13_41_2) 2004 e_1_2_13_28_2 e_1_2_13_27_2 e_1_2_13_29_2 |
| References_xml | – reference: Nocedal J, Wright SJ. Numerical Optimization (2nd edn). Springer: Berlin, 2006. – reference: Kerschen G, Golinval J-C, Vakakis AF, Bergman LA. The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview. Nonlinear Dynamics 2005; 41:147-169. – reference: Antoulas AC. Approximation of Large-scale Dynamical Systems. Society for Industrial and Applied Mathematics: Philadelphia, PA, 2005. – reference: Kelley CT. Iterative Methods for Linear and Nonlinear Equations. Society for Industrial and Applied Mathematics: Philadelphia, PA, 1995. – reference: Bui-Thanh T, Willcox K, Ghattas O. Model reduction for large-scale systems with high-dimensional parametric input space. SIAM Journal on Scientific Computing 2008; 30(6):3270-3288. – reference: Nguyen NC, Peraire J. An efficient reduced-order modeling approach for non-linear parametrized partial differential equations. International Journal for Numerical Methods in Engineering 2008; 76:27-55. – reference: Epureanu BI. A parametric analysis of reduced order models of viscous flows in turbomachinery. Journal of Fluids and Structures 2003; 17:971-982. – reference: Rowley C, Colonius T, Murray R. Model reduction for compressible flows using POD and Galerkin projection. Physica D: Nonlinear Phenomena 2004; 189(1-2):115-129. – reference: Holmes P, Lumley J, Berkooz G. Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge University Press: Cambridge, 1996. – reference: Amsallem D, Carlberg K, Cortial J, Farhat C. A method for interpolating on manifolds structural dynamics reduced-order models. International Journal for Numerical Methods in Engineering 2009; 78:275-295. – reference: Amsallem D, Farhat C. An interpolation method for adapting reduced-order models and application to aeroelasticity. AIAA Journal 2008; 46:1803-1813. – reference: Grepl MA, Maday Y, Nguyen NC, Patera AT. Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations. Mathematical Modelling and Numerical Analysis 2007; 41(3):575-605. – reference: Golub GH, Loan CFV. Matrix Computations (3rd edn). Johns Hopkins University Press: Baltimore, MD, 1996. – reference: Rozza G, Huynh DBP, Patera AT. Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Archives of Computational Methods in Engineering 2007; 15(3):1-47. – reference: Barrault M, Maday Y, Nguyen N, Patera A. An 'empirical interpolation' method: application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus Mathématique Académie des Sciences 2004; 339(9):667-672. – reference: Sirovich L. Turbulence and the dynamics of coherent structures. I-coherent structures. Quarterly of Applied Mathematics 1987; 45:561-571. – reference: Saad Y. Iterative Methods for Sparse Linear Systems (2nd edn). Society for Industrial and Applied Mathematics: Philadelphia, PA, 2003. – reference: Willcox K. Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition. Computers and Fluids 2006; 35(2):208-226. – reference: Han S, Feeny BF. Enhanced proper orthogonal decomposition for the modal analysis of homogeneous structures. Journal of Vibration and Control 2002; 8(1):19-40. – reference: Everson R, Sirovich L. Karhunen-Loeve procedure for gappy data. Journal of the Optical Society of America A 1995; 12(8):1657-1664. – reference: Rathinam M, Petzold LR. A new look at proper orthogonal decomposition. SIAM Journal on Numerical Analysis 2003; 41(5):1893-1925. – reference: Thomas JP, Dowell EH, Hall KC. Three-dimensional transonic aeroelasticity using proper orthogonal decomposition-based reduced order models. Journal of Aircraft 2003; 40(3):544-551. – reference: Kim T, Hong M, Bhatia KB, SenGupta G. Aeroelastic model reduction for affordable computational fluid dynamics-based flutter analysis. AIAA Journal 2005; 43(12):2487-2495. – reference: Willcox K, Peraire J. Balanced model reduction via the proper orthogonal decomposition. AIAA Journal 2002; 40(11):2323-2330. – reference: Veroy K, Patera AT. Certified real-time solution of the parametrized steady incompressible Navier-Stokes equations: rigorous reduced-basis a posteriori error bounds. International Journal for Numerical Methods in Fluids 2005; 47(8):773-788. – reference: Lieu T, Farhat C. Adaptation of aeroelastic reduced-order models and application to an F-16 configuration. AIAA Journal 2007; 45:1244-1269. – reference: Galbally D, Fidkowski K, Willcox K, Ghattas O. Non-linear model reduction for uncertainty quantification in large-scale inverse problems. International Journal for Numerical Methods in Engineering 2010; 81(12):1581-1608. – reference: Venturi D, Karniadakis G. Gappy data and reconstruction procedures for flow past a cylinder. Journal of Fluid Mechanics 2004; 519:315-336. – reference: Bergmann M, Cordier L, Brancher J-P. Optimal rotary control of the cylinder wake using proper orthogonal decomposition reduced-order model. Physics of Fluids 2005; 17(9):80-101. – reference: Queipo N, Haftka R, Shyy W, Goel T, Vaidyanathan R, Tucker P. Surrogate-based analysis and optimization. Progress in Aerospace Sciences 2005; 41(1):1-28. – reference: Hall KC, Thomas JP, Dowell EH. Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows. AIAA Journal 2000; 38(2):1853-1862. – reference: Astrid P, Weiland S, Willcox K, Backx T. Missing point estimation in models described by proper orthogonal decomposition. IEEE Transactions on Automatic Control 2008; 53(10):2237-2251. – reference: Amabili M, Sarkar A, Paidoussis MP. Reduced-order models for nonlinear vibrations of cylindrical shells via the proper orthogonal decomposition method. Journal of Fluids and Structures 2003; 18(2):227-250. – reference: Lieu T, Farhat C, Lesoinne M. Reduced-order fluid/structure modeling of a complete aircraft configuration. Computer Methods in Applied Mechanics and Engineering 2006; 195:5730-5742. – reference: Bui-Thanh T, Damodaran M, Willcox K. Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. AIAA Journal 2004; 42(8):1505-1516. – year: 2009 – volume: 17 start-page: 80 issue: 9 year: 2005 end-page: 101 article-title: Optimal rotary control of the cylinder wake using proper orthogonal decomposition reduced‐order model publication-title: Physics of Fluids – volume: 40 start-page: 2323 issue: 11 year: 2002 end-page: 2330 article-title: Balanced model reduction via the proper orthogonal decomposition publication-title: AIAA Journal – volume: 45 start-page: 561 year: 1987 end-page: 571 article-title: Turbulence and the dynamics of coherent structures. I—coherent structures publication-title: Quarterly of Applied Mathematics – year: 2005 – volume: 45 start-page: 1244 year: 2007 end-page: 1269 article-title: Adaptation of aeroelastic reduced‐order models and application to an F‐16 configuration publication-title: AIAA Journal – volume: 18 start-page: 227 issue: 2 year: 2003 end-page: 250 article-title: Reduced‐order models for nonlinear vibrations of cylindrical shells via the proper orthogonal decomposition method publication-title: Journal of Fluids and Structures – volume: 339 start-page: 667 issue: 9 year: 2004 end-page: 672 article-title: An ‘empirical interpolation’ method: application to efficient reduced‐basis discretization of partial differential equations publication-title: Comptes Rendus Mathématique Académie des Sciences – year: 2003 – volume: 35 start-page: 208 issue: 2 year: 2006 end-page: 226 article-title: Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition publication-title: Computers and Fluids – year: 1996 – volume: 47 start-page: 773 issue: 8 year: 2005 end-page: 788 article-title: Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds publication-title: International Journal for Numerical Methods in Fluids – volume: 41 start-page: 575 issue: 3 year: 2007 end-page: 605 article-title: Efficient reduced‐basis treatment of nonaffine and nonlinear partial differential equations publication-title: Mathematical Modelling and Numerical Analysis – volume: 41 start-page: 1 issue: 1 year: 2005 end-page: 28 article-title: Surrogate‐based analysis and optimization publication-title: Progress in Aerospace Sciences – volume: 8 start-page: 19 issue: 1 year: 2002 end-page: 40 article-title: Enhanced proper orthogonal decomposition for the modal analysis of homogeneous structures publication-title: Journal of Vibration and Control – volume: 41 start-page: 1893 issue: 5 year: 2003 end-page: 1925 article-title: A new look at proper orthogonal decomposition publication-title: SIAM Journal on Numerical Analysis – start-page: 3705 year: 2004 end-page: 3710 – volume: 78 start-page: 275 year: 2009 end-page: 295 article-title: A method for interpolating on manifolds structural dynamics reduced‐order models publication-title: International Journal for Numerical Methods in Engineering – volume: 19 year: 2004 – volume: 15 start-page: 1 issue: 3 year: 2007 end-page: 47 article-title: Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations publication-title: Archives of Computational Methods in Engineering – volume: 53 start-page: 2237 issue: 10 year: 2008 end-page: 2251 article-title: Missing point estimation in models described by proper orthogonal decomposition publication-title: IEEE Transactions on Automatic Control – volume: 30 start-page: 3270 issue: 6 year: 2008 end-page: 3288 article-title: Model reduction for large‐scale systems with high‐dimensional parametric input space publication-title: SIAM Journal on Scientific Computing – volume: 17 start-page: 971 year: 2003 end-page: 982 article-title: A parametric analysis of reduced order models of viscous flows in turbomachinery publication-title: Journal of Fluids and Structures – volume: 81 start-page: 1581 issue: 12 year: 2010 end-page: 1608 article-title: Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems publication-title: International Journal for Numerical Methods in Engineering – year: 1984 – volume: 12 start-page: 1657 issue: 8 year: 1995 end-page: 1664 article-title: Karhunen–Loeve procedure for gappy data publication-title: Journal of the Optical Society of America A – volume: 189 start-page: 115 year: 2004 end-page: 129 article-title: Model reduction for compressible flows using POD and Galerkin projection publication-title: Physica D: Nonlinear Phenomena – volume: 41 start-page: 147 year: 2005 end-page: 169 article-title: The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview publication-title: Nonlinear Dynamics – start-page: 1529 year: 2005 end-page: 1564 – volume: 43 start-page: 2487 issue: 12 year: 2005 end-page: 2495 article-title: Aeroelastic model reduction for affordable computational fluid dynamics‐based flutter analysis publication-title: AIAA Journal – year: 2006 – volume: 40 start-page: 544 issue: 3 year: 2003 end-page: 551 article-title: Three‐dimensional transonic aeroelasticity using proper orthogonal decomposition‐based reduced order models publication-title: Journal of Aircraft – year: 1995 – volume: 38 start-page: 1853 issue: 2 year: 2000 end-page: 1862 article-title: Proper orthogonal decomposition technique for transonic unsteady aerodynamic flows publication-title: AIAA Journal – volume: 519 start-page: 315 year: 2004 end-page: 336 article-title: Gappy data and reconstruction procedures for flow past a cylinder publication-title: Journal of Fluid Mechanics – volume: 46 start-page: 1803 year: 2008 end-page: 1813 article-title: An interpolation method for adapting reduced‐order models and application to aeroelasticity publication-title: AIAA Journal – volume: 195 start-page: 5730 year: 2006 end-page: 5742 article-title: Reduced‐order fluid/structure modeling of a complete aircraft configuration publication-title: Computer Methods in Applied Mechanics and Engineering – volume: 42 start-page: 1505 issue: 8 year: 2004 end-page: 1516 article-title: Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition publication-title: AIAA Journal – start-page: 197 year: 2007 end-page: 216 – volume: 76 start-page: 27 year: 2008 end-page: 55 article-title: An efficient reduced‐order modeling approach for non‐linear parametrized partial differential equations publication-title: International Journal for Numerical Methods in Engineering – ident: e_1_2_13_16_2 doi: 10.1016/j.cma.2005.08.026 – ident: e_1_2_13_9_2 doi: 10.2514/6.1999-655 – ident: e_1_2_13_4_2 doi: 10.1137/1.9780898718713 – ident: e_1_2_13_44_2 doi: 10.1017/S0022112004001338 – start-page: 1529 volume-title: Handbook of Materials Modeling year: 2005 ident: e_1_2_13_7_2 – ident: e_1_2_13_32_2 doi: 10.1016/j.paerosci.2005.02.001 – ident: e_1_2_13_45_2 doi: 10.1016/j.compfluid.2004.11.006 – ident: e_1_2_13_15_2 doi: 10.2514/1.11246 – ident: e_1_2_13_31_2 doi: 10.1016/j.crma.2004.08.006 – ident: e_1_2_13_30_2 doi: 10.1364/JOSAA.12.001657 – volume-title: Matrix Computations year: 1996 ident: e_1_2_13_36_2 – ident: e_1_2_13_47_2 doi: 10.23919/ACC.2004.1384488 – ident: e_1_2_13_6_2 doi: 10.2514/6.2003-3847 – ident: e_1_2_13_11_2 doi: 10.2514/2.867 – ident: e_1_2_13_22_2 doi: 10.1007/s11071-005-2803-2 – ident: e_1_2_13_12_2 doi: 10.2514/2.1570 – ident: e_1_2_13_20_2 doi: 10.1177/1077546302008001518 – ident: e_1_2_13_14_2 doi: 10.2514/2.3128 – ident: e_1_2_13_28_2 – ident: e_1_2_13_27_2 doi: 10.1109/TAC.2008.2006102 – ident: e_1_2_13_26_2 doi: 10.1002/nme.2309 – ident: e_1_2_13_39_2 doi: 10.1137/070694855 – ident: e_1_2_13_13_2 doi: 10.1016/S0889-9746(03)00044-6 – ident: e_1_2_13_21_2 doi: 10.1016/j.jfluidstructs.2003.06.002 – ident: e_1_2_13_42_2 doi: 10.2514/6.2003-4213 – ident: e_1_2_13_38_2 doi: 10.1137/1.9781611970944 – volume: 78 start-page: 275 year: 2009 ident: e_1_2_13_23_2 article-title: A method for interpolating on manifolds structural dynamics reduced‐order models publication-title: International Journal for Numerical Methods in Engineering – ident: e_1_2_13_17_2 doi: 10.2514/1.24512 – ident: e_1_2_13_35_2 doi: 10.1137/1.9780898718003 – ident: e_1_2_13_18_2 doi: 10.2514/1.35374 – volume-title: SAE Paper 840300 year: 1984 ident: e_1_2_13_40_2 – ident: e_1_2_13_2_2 doi: 10.1090/qam/910462 – ident: e_1_2_13_5_2 doi: 10.1007/BF03024948 – ident: e_1_2_13_25_2 doi: 10.1051/m2an:2007031 – ident: e_1_2_13_8_2 doi: 10.1002/fld.867 – ident: e_1_2_13_10_2 doi: 10.2514/6.2000-2545 – ident: e_1_2_13_19_2 doi: 10.1063/1.2033624 – ident: e_1_2_13_46_2 doi: 10.2514/6.2006-1819 – ident: e_1_2_13_43_2 doi: 10.2514/1.2159 – start-page: 197 volume-title: Proceedings of the Second Sandia Workshop of PDE‐Constrained Optimization year: 2007 ident: e_1_2_13_24_2 – volume-title: Numerical Optimization year: 2006 ident: e_1_2_13_37_2 – ident: e_1_2_13_3_2 doi: 10.1017/CBO9780511622700 – ident: e_1_2_13_29_2 doi: 10.1002/nme.2746 – volume-title: The Aerodynamics of Heavy Vehicles: Trucks, Buses, and Trains year: 2004 ident: e_1_2_13_41_2 – ident: e_1_2_13_34_2 doi: 10.1137/S0036142901389049 – ident: e_1_2_13_33_2 doi: 10.1016/j.physd.2003.03.001 |
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| Snippet | A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete non‐linear static or dynamic computational model in view of enabling... A Petrov-Galerkin projection method is proposed for reducing the dimension of a discrete non-linear static or dynamic computational model in view of enabling... |
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| SubjectTerms | Approximation compressive approximation Computation Computational efficiency discrete non-linear systems Exact sciences and technology Fluid dynamics Fundamental areas of phenomenology (including applications) gappy data Invariants Mathematical models Mathematics Methods of scientific computing (including symbolic computation, algebraic computation) non-linear model reduction Nonlinear dynamics Nonlinearity Numerical analysis. Scientific computation Petrov-Galerkin projection Physics Projection proper orthogonal decomposition Sciences and techniques of general use Solid mechanics Structural and continuum mechanics Turbulent flows, convection, and heat transfer Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...) |
| Title | Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations |
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