The Citing articles tool gives a list of articles citing the current article. The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
This article has been cited by the following article(s):
Recent Advances in Computational Engineering
Christopher Spannring, Sebastian Ullmann and Jens Lang Lecture Notes in Computational Science and Engineering, Recent Advances in Computational Engineering 124 145 (2018) https://doi.org/10.1007/978-3-319-93891-2_9
Parameter estimation with model order reduction for elliptic differential equations
Multivariate predictions of local reduced‐order‐model errors and dimensions
Azam Moosavi, Răzvan Ştefănescu and Adrian Sandu International Journal for Numerical Methods in Engineering 113(3) 512 (2018) https://doi.org/10.1002/nme.5624
Model reduction using L1‐norm minimization as an application to nonlinear hyperbolic problems
Some a posteriori error bounds for reduced-order modelling of (non-)parametrized linear systems
Lihong Feng, Athanasios C. Antoulas and Peter Benner ESAIM: Mathematical Modelling and Numerical Analysis 51(6) 2127 (2017) https://doi.org/10.1051/m2an/2017014
Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models
Error modeling for surrogates of dynamical systems using machine learning
Sumeet Trehan, Kevin T. Carlberg and Louis J. Durlofsky International Journal for Numerical Methods in Engineering 112(12) 1801 (2017) https://doi.org/10.1002/nme.5583
Accelerated Solution of Discrete Ordinates Approximation to the Boltzmann Transport Equation for a Gray Absorbing–Emitting Medium Via Model Reduction
John Tencer, Kevin Carlberg, Marvin Larsen and Roy Hogan Journal of Heat Transfer 139(12) 122701 (2017) https://doi.org/10.1115/1.4037098
Reduced Basis Methods for Uncertainty Quantification
Peng Chen, Alfio Quarteroni and Gianluigi Rozza SIAM/ASA Journal on Uncertainty Quantification 5(1) 813 (2017) https://doi.org/10.1137/151004550
A posteriori error estimation and adaptive strategy for PGD model reduction applied to parametrized linear parabolic problems
Ludovic Chamoin, Florent Pled, Pierre-Eric Allier and Pierre Ladevèze Computer Methods in Applied Mechanics and Engineering 327 118 (2017) https://doi.org/10.1016/j.cma.2017.08.047
Dynamical Model Reduction Method for Solving Parameter-Dependent Dynamical Systems
A Certified Trust Region Reduced Basis Approach to PDE-Constrained Optimization
Elizabeth Qian, Martin Grepl, Karen Veroy and Karen Willcox SIAM Journal on Scientific Computing 39(5) S434 (2017) https://doi.org/10.1137/16M1081981
A reduced order model‐based preconditioner for the efficient solution of transient diffusion equations
Damiano Pasetto, Massimiliano Ferronato and Mario Putti International Journal for Numerical Methods in Engineering 109(8) 1159 (2017) https://doi.org/10.1002/nme.5320
Limited‐memory adaptive snapshot selection for proper orthogonal decomposition
Geoffrey M. Oxberry, Tanya Kostova‐Vassilevska, William Arrighi and Kyle Chand International Journal for Numerical Methods in Engineering 109(2) 198 (2017) https://doi.org/10.1002/nme.5283
Global sensitivity analysis for the boundary control of an open channel
Alexandre Janon, Maëlle Nodet, Christophe Prieur and Clémentine Prieur Mathematics of Control, Signals, and Systems 28(1) (2016) https://doi.org/10.1007/s00498-015-0151-4
A posteriori global error estimator based on the error in the constitutive relation for reduced basis approximation of parametrized linear elastic problems
Isogeometric analysis-based reduced order modelling for incompressible linear viscous flows in parametrized shapes
Filippo Salmoiraghi, Francesco Ballarin, Luca Heltai and Gianluigi Rozza Advanced Modeling and Simulation in Engineering Sciences 3(1) (2016) https://doi.org/10.1186/s40323-016-0076-6
Approximate optimal projection for reduced‐order models
Assad A. Oberai and Jayanth Jagalur‐Mohan International Journal for Numerical Methods in Engineering 105(1) 63 (2016) https://doi.org/10.1002/nme.4963
A reduced basis approach for the parametric low frequency response of submerged viscoelastic structures
2D Burgers equation with large Reynolds number using POD/DEIM and calibration
Yuepeng Wang, Ionel M. Navon, Xinyue Wang and Yue Cheng International Journal for Numerical Methods in Fluids 82(12) 909 (2016) https://doi.org/10.1002/fld.4249
A Goal-Oriented Reduced Basis Methods-Accelerated Generalized Polynomial Chaos Algorithm
Jiahua Jiang, Yanlai Chen and Akil Narayan SIAM/ASA Journal on Uncertainty Quantification 4(1) 1398 (2016) https://doi.org/10.1137/16M1055736
Real-time solution of linear computational problems using databases of parametric reduced-order models with arbitrary underlying meshes
A Reduced Basis Approach for Modeling the Movement of Nuclear Reactor Control Rods
Alberto Sartori, Antonio Cammi, Lelio Luzzi and Gianluigi Rozza Journal of Nuclear Engineering and Radiation Science 2(2) 021019 (2016) https://doi.org/10.1115/1.4031945
A Reduced Radial Basis Function Method for Partial Differential Equations on Irregular Domains
An efficient goal‐oriented sampling strategy using reduced basis method for parametrized elastodynamic problems
K. C. Hoang, P. Kerfriden, B. C. Khoo and S. P. A. Bordas Numerical Methods for Partial Differential Equations 31(2) 575 (2015) https://doi.org/10.1002/num.21932
Accelerating PDE constrained optimization by the reducedbasis method: application to batch chromatography
Reduced Order Methods for Modeling and Computational Reduction
Harbir Antil, Matthias Heinkenschloss and Danny C. Sorensen Reduced Order Methods for Modeling and Computational Reduction 101 (2014) https://doi.org/10.1007/978-3-319-02090-7_4
Reduced Order Methods for Modeling and Computational Reduction
Toni Lassila, Andrea Manzoni, Alfio Quarteroni and Gianluigi Rozza Reduced Order Methods for Modeling and Computational Reduction 235 (2014) https://doi.org/10.1007/978-3-319-02090-7_9
Comparison Between Reduced Basis and Stochastic Collocation Methods for Elliptic Problems
Double greedy algorithms: Reduced basis methods for transport dominated problems
Wolfgang Dahmen, Christian Plesken and Gerrit Welper ESAIM: Mathematical Modelling and Numerical Analysis 48(3) 623 (2014) https://doi.org/10.1051/m2an/2013103
A Space-Time Petrov--Galerkin Certified Reduced Basis Method: Application to the Boussinesq Equations
Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods
Jan S. Hesthaven, Benjamin Stamm and Shun Zhang ESAIM: Mathematical Modelling and Numerical Analysis 48(1) 259 (2014) https://doi.org/10.1051/m2an/2013100
A space-time hp-interpolation-based certified reduced basis method for Burgers' equation
Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations
Răzvan Ştefănescu, Adrian Sandu and Ionel M. Navon International Journal for Numerical Methods in Fluids 76(8) 497 (2014) https://doi.org/10.1002/fld.3946
A mathematical and numerical study of the sensitivity of a reduced order model by POD (ROM–POD), for a 2D incompressible fluid flow
Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen--Loève Expansion
Bernard Haasdonk, Karsten Urban and Bernhard Wieland SIAM/ASA Journal on Uncertainty Quantification 1(1) 79 (2013) https://doi.org/10.1137/120876745
Eduardo Gildin, Mohammadreza Ghasemi, Anastasiya Romanovskay and Yalchin Efendiev (2013) https://doi.org/10.2118/163618-MS
Research on the efficiency of reduced-basis approach in computations of structural problems and its improvements
F Lei, XP Xie, XW Wang and YG Wang Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227(10) 2143 (2013) https://doi.org/10.1177/0954406212470895
Accurate and efficient evaluation of failure probability for partial different equations with random input data