Reduced Basis Greedy Selection Using Random Training Sets
ESAIM: M2AN, 54 5 (2020) 1509-1524
Published online: 2020-07-16
Articles citing this article
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).
Cited article:
This article has been cited by the following article(s):
Numerical investigations of reduced-order models with time-varying parameters generated by hybrid training with automatic hyper-reduction in nonlinear diffusion–reaction systems
Feng BaiMathematics and Computers in Simulation 241 489 (2026)
https://doi.org/10.1016/j.matcom.2025.10.018
Compositional Sparsity, Approximation Classes, and Parametric Transport Equations
Wolfgang DahmenConstructive Approximation 61 (2) 219 (2025)
https://doi.org/10.1007/s00365-024-09699-9
A subspace method based on the Neumann series for the solution of parametric linear systems
Antti Autio and Antti HannukainenComputers & Mathematics with Applications 178 1 (2025)
https://doi.org/10.1016/j.camwa.2024.11.019
Variationally correct neural residual regression for parametric PDEs: on the viability of controlled accuracy
Markus Bachmayr, Wolfgang Dahmen and Mathias OsterIMA Journal of Numerical Analysis (2025)
https://doi.org/10.1093/imanum/draf073
Enhancing speedup in multifidelity training process by design automation to generate online reduced-order models in Convection–Diffusion–Reaction systems
Feng BaiAdvances in Engineering Software 202 103864 (2025)
https://doi.org/10.1016/j.advengsoft.2025.103864
Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations
Jinchao Xu and Xiaofeng XuJournal of Scientific Computing 105 (1) (2025)
https://doi.org/10.1007/s10915-025-03050-5
Structured Optimization-Based Model Order Reduction for Parametric Systems
Paul Schwerdtner and Manuel SchallerSIAM Journal on Scientific Computing 47 (1) A72 (2025)
https://doi.org/10.1137/22M1524928
Polytope Division Method: A Scalable Sampling Method for Problems with High-Dimensional Parameters
Evie Nielen, Oliver Tse and Karen VeroySIAM Journal on Scientific Computing 47 (6) B1424 (2025)
https://doi.org/10.1137/24M1704506
Structure-preserving interpolation reduced-order modelling for efficient simulation of parametric port-Hamiltonian systems
Yu-Han Hu, Zhen Li and Kang-Li XuInternational Journal of Systems Science 1 (2025)
https://doi.org/10.1080/00207721.2025.2546345
A probabilistic reduced basis method for parameter-dependent problems
Marie Billaud-Friess, Arthur Macherey, Anthony Nouy and Clémentine PrieurAdvances in Computational Mathematics 50 (2) (2024)
https://doi.org/10.1007/s10444-024-10114-x
Multiplicative reduced bases for hyperelasticity
Patrice HauretComptes Rendus. Mathématique 362 (G6) 593 (2024)
https://doi.org/10.5802/crmath.584
Influence of sampling on the convergence rates of greedy algorithms for parameter-dependent random variables
Mohamed-Raed Blel, Virginie Ehrlacher and Tony LelièvreMathematics of Computation (2024)
https://doi.org/10.1090/mcom/3979
https://doi.org/10.1016/bs.hna.2024.05.006
An iterative multi‐fidelity approach for model order reduction of multidimensional input parametric PDE systems
Manisha Chetry, Domenico Borzacchiello, Lucas Lestandi and Luisa Rocha Da SilvaInternational Journal for Numerical Methods in Engineering 124 (22) 4941 (2023)
https://doi.org/10.1002/nme.7333
2328 73 (2023)
https://doi.org/10.1007/978-3-031-29563-8_2
Dual natural-norm a posteriori error estimators for reduced basis approximations to parametrized linear equations
P. Edel and Y. MadayMathematical Models and Methods in Applied Sciences 33 (06) 1215 (2023)
https://doi.org/10.1142/S0218202523500288
A reduced order modeling approach with Petrov–Galerkin projection based on hybrid snapshot simulation in Semilinear-PDEs
Feng BaiJournal of Computational Science 67 101969 (2023)
https://doi.org/10.1016/j.jocs.2023.101969
Nonlinear Reduced Models for State and Parameter Estimation
Albert Cohen, Wolfgang Dahmen, Olga Mula and James NicholsSIAM/ASA Journal on Uncertainty Quantification 10 (1) 227 (2022)
https://doi.org/10.1137/20M1380818
Recent Advances in Industrial and Applied Mathematics
Albert Cohen, Wolfgang Dahmen and Ron DeVoreSEMA SIMAI Springer Series, Recent Advances in Industrial and Applied Mathematics 1 57 (2022)
https://doi.org/10.1007/978-3-030-86236-7_4
A Stochastic Discrete Empirical Interpolation Approach for Parameterized Systems
Daheng Cai, Chengbin Yao and Qifeng LiaoSymmetry 14 (3) 556 (2022)
https://doi.org/10.3390/sym14030556
Training and projecting: A reduced basis method emulator for many-body physics
Edgard Bonilla, Pablo Giuliani, Kyle Godbey and Dean LeePhysical Review C 106 (5) (2022)
https://doi.org/10.1103/PhysRevC.106.054322
A ROM-accelerated parallel-in-time preconditioner for solving all-at-once systems in unsteady convection-diffusion PDEs
Jun Liu and Zhu WangApplied Mathematics and Computation 416 126750 (2022)
https://doi.org/10.1016/j.amc.2021.126750
A reduced order modeling method based on GNAT-embedded hybrid snapshot simulation
Feng Bai and Yi WangMathematics and Computers in Simulation 199 100 (2022)
https://doi.org/10.1016/j.matcom.2022.03.006
A FOM/ROM Hybrid Approach for Accelerating Numerical Simulations
Lihong Feng, Guosheng Fu and Zhu WangJournal of Scientific Computing 89 (3) (2021)
https://doi.org/10.1007/s10915-021-01668-9
Nonlinear methods for model reduction
Andrea Bonito, Albert Cohen, Ronald DeVore, et al.ESAIM: Mathematical Modelling and Numerical Analysis 55 (2) 507 (2021)
https://doi.org/10.1051/m2an/2020057
On the stability and accuracy of the empirical interpolation method and gravitational wave surrogates
Manuel Tiglio and Aarón VillanuevaClassical and Quantum Gravity 38 (13) 135011 (2021)
https://doi.org/10.1088/1361-6382/abf894
Optimal Reduced Model Algorithms for Data-Based State Estimation
Albert Cohen, Wolfgang Dahmen, Ronald DeVore, et al.SIAM Journal on Numerical Analysis 58 (6) 3355 (2020)
https://doi.org/10.1137/19M1255185