# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## Pages

## Posts

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## portfolio

## publications

*bib*

[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.

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

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[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.

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[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.

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

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[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.

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[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.

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[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.

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[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.

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[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.

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

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

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[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.

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

## talks

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## teaching

[1] *MATLAB*

*University of Trieste*, 2019

[2] *ROM in bifurcating parametrised non-linear equations*

*SISSA*, 2019

[3] *Dynamics and bifurcation*

*EPFL*, 2022

[4] *Computational Mechanics by Reduced Order Methods*

*SISSA*, 2022

[5] *Summer School on ROMs in Computational Fluid Dynamics*

*SISSA*, 2022

[6] *Analyse III*

*EPFL*, 2022

[7] *Analyse III*

*EPFL*, 2023