Posts by Collection

[1] D. Huynh, F. Pichi, G. Rozza. Reduced Basis Approximation and A Posteriori Error Estimation: Applications to Elasticity Problems in Several Parametric Settings. In the proceedings of Numerical Methods for PDEs: State of the Art Techniques, 2018.

[5] F. Pichi, G. Rozza. Reduced basis approaches for parametrized bifurcation problems held by non-linear Von Karman equations. Journal of Scientific Computing, 2019.

[6] F. Pichi, A. Quaini, G. Rozza. A Reduced Order Modeling Technique to Study Bifurcating Phenomena: Application to the Gross-Pitaevskii Equation. SIAM Journal on Scientific Computing, 2020.

[7] M. Pintore, F. Pichi, M. Hess, G. Rozza, C. Canuto. Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method. Advances in Computational Mathematics, 2020.

[8] F. Pichi, F. Ballarin, G. Rozza, J. Hesthaven. Artificial neural network for bifurcating phenomena modelled by nonlinear parametrized PDEs. PAMM, 2021.

[9] F. Pichi, M. Strazzullo, F. Ballarin, G. Rozza. Driving Bifurcating Parametrized Nonlinear PDEs by Optimal Control Strategies: Application to Navier-Stokes Equations with Model Order Reduction. ESAIM: Mathematical Modelling and Numerical Analysis, 2022.

[10] M. Khamlich, F. Pichi, G. Rozza. Model Order Reduction for Bifurcating Phenomena in Fluid-Structure Interaction Problems. International Journal for Numerical Methods in Fluids, 2022.

[2] F. Pichi, M. Strazzullo, F. Ballarin, G. Rozza. Chapter 2: Finite Element-Based Reduced Basis Method in Computational Fluid Dynamics. In the proceedings of Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, 2022.

[3] F. Pichi, F. Ballarin, G. Rozza. Chapter 5: Reduced Basis Approaches to Bifurcating Nonlinear Parametrized Partial Differential Equations. In the proceedings of Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, 2022.

[4] M. Khamlich, F. Pichi, G. Rozza. Chapter 15: Reduced Order Models for Bifurcating Phenomena in Fluid-Structure Interaction Problems. In the proceedings of Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, 2022.

[11] F. Pichi, F. Ballarin, G. Rozza, J. Hesthaven. An Artificial Neural Network Approach to Bifurcating Phenomena in Computational Fluid Dynamics. Computers & Fluids, 2023.

[13] F. Pichi, G. Rozza. Reduced Order Models for the Buckling of Hyperelastic Beams. arXiv:2305.19764, 2023.

[14] M. Khamlich, F. Pichi, G. Rozza. Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel. arXiv:2308.13840, 2023.

[12] Federico Pichi, Beatriz Moya, Jan Hesthaven. A Graph Convolutional Autoencoder Approach to Model Order Reduction for Parametrized PDEs. Journal of Computational Physics, 2024.