[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.

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

[12] F. Pichi, B. Moya, J. Hesthaven. A Graph Convolutional Autoencoder Approach to Model Order Reduction for Parametrized PDEs. arXiv:2305.08573, 2023.


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

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

[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.

[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.

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