@booklet {2022, title = {Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics}, year = {2022}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @booklet {2022, title = {A Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation}, year = {2022}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @article {2022, title = {Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier{\textendash}Stokes equations with model order reduction}, journal = {ESAIM: M2AN}, volume = {56}, year = {2022}, month = {2022///}, pages = {1361 - 1400}, url = {https://doi.org/10.1051/m2an/2022044}, author = {Federico Pichi and Maria Strazzullo and F. Ballarin and Gianluigi Rozza} } @unpublished {2021, title = {A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition}, year = {2021}, author = {Eleonora Donadini and Maria Strazzullo and Marco Tezzele and Gianluigi Rozza} } @booklet {2021, title = {A Dimensionality Reduction Approach for Convolutional Neural Networks}, year = {2021}, author = {Laura Meneghetti and Nicola Demo and Gianluigi Rozza} } @conference {2021, title = {Discontinuous Galerkin Model Order Reduction of Geometrically Parametrized Stokes Equation}, booktitle = {Numerical Mathematics and Advanced Applications ENUMATH 2019}, year = {2021}, month = {2021//}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

The present work focuses on the geometric parametrization and the reduced order modeling of the Stokes equation. We discuss the concept of a parametrized geometry and its application within a reduced order modeling technique. The full order model is based on the discontinuous Galerkin method with an interior penalty formulation. We introduce the broken Sobolev spaces as well as the weak formulation required for an affine parameter dependency. The operators are transformed from a fixed domain to a parameter dependent domain using the affine parameter dependency. The proper orthogonal decomposition is used to obtain the basis of functions of the reduced order model. By using the Galerkin projection the linear system is projected onto the reduced space. During this process, the offline-online decomposition is used to separate parameter dependent operations from parameter independent operations. Finally this technique is applied to an obstacle test problem.The numerical outcomes presented include experimental error analysis, eigenvalue decay and measurement of online simulation time.

}, isbn = {978-3-030-55874-1}, author = {Nirav Shah and Martin W. Hess and Gianluigi Rozza}, editor = {Vermolen, Fred J. and Vuik, Cornelis} } @article {2021, title = {A dynamic mode decomposition extension for the forecasting of parametric dynamical systems}, journal = {arXiv preprint arXiv:2110.09155}, year = {2021}, author = {Francesco Andreuzzi and Nicola Demo and Gianluigi Rozza} } @article {2020, title = {Data-driven POD-Galerkin reduced order model for turbulent flows}, journal = {Journal of Computational Physics}, volume = {416}, year = {2020}, pages = {109513}, abstract = {

In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to $Re=O(10^5)$.

}, doi = {10.1016/j.jcp.2020.109513}, url = {https://arxiv.org/abs/1907.09909}, author = {Saddam Hijazi and Giovanni Stabile and Andrea Mola and Gianluigi Rozza} } @article {TezzeleSalmoiraghiMolaRozza2018, title = {Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems}, journal = {Advanced Modeling and Simulation in Engineering Sciences}, volume = {5}, number = {1}, year = {2018}, month = {Sep}, pages = {25}, abstract = {

We present the results of the first application in the naval architecture field of a methodology based on active subspaces properties for parameters space reduction. The physical problem considered is the one of the simulation of the hydrodynamic flow past the hull of a ship advancing in calm water. Such problem is extremely relevant at the preliminary stages of the ship design, when several flow simulations are typically carried out by the engineers to assess the dependence of the hull total resistance on the geometrical parameters of the hull, and others related with flows and hull properties. Given the high number of geometric and physical parameters which might affect the total ship drag, the main idea of this work is to employ the active subspaces properties to identify possible lower dimensional structures in the parameter space. Thus, a fully automated procedure has been implemented to produce several small shape perturbations of an original hull CAD geometry, in order to exploit the resulting shapes to run high fidelity flow simulations with different structural and physical parameters as well, and then collect data for the active subspaces analysis. The free form deformation procedure used to morph the hull shapes, the high fidelity solver based on potential flow theory with fully nonlinear free surface treatment, and the active subspaces analysis tool employed in this work have all been developed and integrated within SISSA mathLab as open source tools. The contribution will also discuss several details of the implementation of such tools, as well as the results of their application to the selected target engineering problem.

}, doi = {10.1186/s40323-018-0118-3}, author = {Marco Tezzele and Filippo Salmoiraghi and Andrea Mola and Gianluigi Rozza} }