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Nicoḷ Ripamonti
Nicoḷ Ripamonti
Scientist, Hitachi Energy Corporate Research
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Cited by
Cited by
Year
Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism
Q Wang, N Ripamonti, JS Hesthaven
Journal of Computational Physics 410, 109402, 2020
1082020
Rank-adaptive structure-preserving model order reduction of Hamiltonian systems
JS Hesthaven, C Pagliantini, N Ripamonti
ESAIM: Mathematical Modelling and Numerical Analysis 56 (2), 617-650, 2022
262022
Conservative model order reduction for fluid flow
BM Afkham, N Ripamonti, Q Wang, JS Hesthaven
Quantification of Uncertainty: Improving Efficiency and Technology: QUIET …, 2020
222020
Rank-adaptive structure-preserving reduced basis methods for Hamiltonian systems
JS Hesthaven, C Pagliantini, N Ripamonti
arXiv preprint arXiv:2007.13153, 2020
152020
Structure-preserving model order reduction of Hamiltonian systems
JS Hesthaven, C Pagliantini, N Ripamonti
arXiv preprint arXiv:2109.12367, 2021
102021
Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equatio
JS Hesthaven, C Pagliantini, N Ripamonti
arXiv preprint arXiv:2201.05555, 2022
72022
Conservative model order reduction for fluid flow
B Maboudi Afkham, N Ripamonti, Q Wang, JS Hesthaven
Quantification of Uncertainty: Improving Efficiency and Technology, 282, 2020
12020
Energy-preserving model reduction of fluid flows
N RIPAMONTI
Italy, 2018
12018
Structure-preserving approaches and data-driven closure modeling for model order reduction
N Ripamonti
EPFL, 2022
2022
Libreria C++ per la riduzione di modello di problemi simplettici
N Ripamonti
2017
MCSS
C Bigoni, B Bonev, P Cazeaux, N Discacciati, J Duan, P Gatto, H Gorji, ...
Structure Preserving Reduced Order Models
BM Afkham, JS Hesthaven, N Ripamonti
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