| 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 | 123 | 2020 |
| 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 | 44 | 2022 |
| Conservative model order reduction for fluid flow BM Afkham, N Ripamonti, Q Wang, JS Hesthaven Quantification of Uncertainty: Improving Efficiency and Technology: QUIET …, 2020 | 23 | 2020 |
| Structure-preserving model order reduction of Hamiltonian systems JS Hesthaven, C Pagliantini, N Ripamonti arXiv preprint arXiv:2109.12367 10, 2021 | 12 | 2021 |
| Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equatio JS Hesthaven, C Pagliantini, N Ripamonti arXiv preprint arXiv:2201.05555, 2022 | 10 | 2022 |
| Energy-preserving model reduction of fluid flows N RIPAMONTI Politecnico di Milano, 2016 | 1 | 2016 |
| 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 | | |
