| An artificial neural network approach to bifurcating phenomena in computational fluid dynamics F Pichi, F Ballarin, G Rozza, JS Hesthaven Computers & Fluids 254, 105813, 2023 | 44 | 2023 |
| Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier–Stokes equations with model order reduction F Pichi, M Strazzullo, F Ballarin, G Rozza ESAIM: Mathematical Modelling and Numerical Analysis 56 (4), 1361-1400, 2022 | 26 | 2022 |
| Reduced basis approaches for parametrized bifurcation problems held by non-linear Von Kármán equations F Pichi, G Rozza Journal of Scientific Computing 81, 112-135, 2019 | 25 | 2019 |
| Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method M Pintore, F Pichi, M Hess, G Rozza, C Canuto Advances in Computational Mathematics 47, 1-39, 2021 | 18 | 2021 |
| A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F Pichi, B Moya, JS Hesthaven Journal of Computational Physics 501, 112762, 2024 | 17 | 2024 |
| A Reduced Order Modeling technique to study bifurcating phenomena: application to the Gross-Pitaevskii equation F Pichi, A Quaini, G Rozza SIAM Journal on Scientific Computing 42 (5), B1115–B1135, 2020 | 14 | 2020 |
| Model order reduction for bifurcating phenomena in fluid‐structure interaction problems M Khamlich, F Pichi, G Rozza International Journal for Numerical Methods in Fluids 94 (10), 1611-1640, 2022 | 12 | 2022 |
| Reduced order models for parametric bifurcation problems in nonlinear PDEs F Pichi SISSA, 2020 | 11 | 2020 |
| Artificial neural network for bifurcating phenomena modelled by nonlinear parametrized PDEs F Pichi, F Ballarin, G Rozza, JS Hesthaven PAMM 20 (S1), e202000350, 2021 | 9 | 2021 |
| Reduced basis approximation and a posteriori error estimation: applications to elasticity problems in several parametric settings DBP Huynh, F Pichi, G Rozza Numerical Methods for PDEs: State of the Art Techniques, 203-247, 2018 | 6 | 2018 |
| Reduced order models for the buckling of hyperelastic beams F Pichi, G Rozza arXiv preprint arXiv:2305.19764, 2023 | 3 | 2023 |
| Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel M Khamlich, F Pichi, G Rozza arXiv preprint arXiv:2308.13840, 2023 | 2 | 2023 |
| Chapter 5: Reduced Basis Approaches to Bifurcating Nonlinear Parametrized Partial Differential Equations F Pichi, F Ballarin, G Rozza Advanced Reduced Order Methods and Applications in Computational Fluid …, 2022 | 1 | 2022 |
| Chapter 2: Finite Element-Based Reduced Basis Method in Computational Fluid Dynamics F Pichi, M Strazzullo, F Ballarin, G Rozza Advanced Reduced Order Methods and Applications in Computational Fluid …, 2022 | 1 | 2022 |
| A stochastic perturbation approach to nonlinear bifurcating problems IC Gonnella, M Khamlich, F Pichi, G Rozza arXiv preprint arXiv:2402.16803, 2024 | | 2024 |
| Real Time Reduced Order Computational Mechanics Parametric PDEs Worked Out Problems G Rozza, F Ballarin, L Scandurra, F Pichi SISSA Springer Series 5, 2024 | | 2024 |
| Chapter 15: Reduced Order Models for Bifurcating Phenomena in Fluid-Structure Interaction Problems M Khamlich, F Pichi, G Rozza Advanced Reduced Order Methods and Applications in Computational Fluid …, 2022 | | 2022 |
| Reduced order methods for nonlinear parametric problems with branching solutions F Pichi, MW Hess, A Quaini, G Rozza ScienceOpen Posters, 2018 | | 2018 |
| Enhancing Deep Learning for Slow-Decaying Problems: An Optimal Transport-based Approach with Sinkhorn Loss and Wasserstein Kernel M Khamlich, F Pichi, G Rozza | | |
| DEEP LEARNING AND REDUCED ORDER MODELING FOR DIFFERENTIAL EQUATIONS NR FRANCO, F PICHI, S FRESCA | | |
Gianluigi RozzaFull Professor of Numerical Analysis, SISSA, Int. School for Adv. Studies, Italy and ERC PIEmail verificata su sissa.it
