Group activities in a glance

• Overview
• Computational science for engineering problems
• Computational science and engineering for Naval/Nautical applications
• Computational science and engineering for cardiovascular applications
• New Research topics in the field of Artificial Intelligence
• SISSA mathlab projects

Research Interests

• Numerical Analysis, Numerical Simulation, Scientific Computing
• Reduced Order Modelling and Methods: special focus on viscous flows and complex geometrical parametrizations
• Efficient Reduced-Basis Methods for parametrized PDEs and a posteriori error estimation
• Computational Fluid Dynamics (Aero-Naval-Mechanical Engineering, Blood Flows (Haemodynamics), Environmental Fluid Dynamics)
• Linear and Non-linear Elasticity
• Heat and Mass Transfer
• Potential Flows
• Fluid-Structure Interaction Problems
• Small Perturbation Theory in Fluid Mechanics and Linearized Shape design
• Parametrized Navier-Stokes Equations: Bifurcations and stability of flows
• Optimal Control, Flow Control based on PDEs
• Optimal Shape Design; Shape Optimization; Shape Reconstruction; Shape Registration
• Free-Form Deformations and Radial Basis Functions
• Uncertainty quantification, data assimilation, parameter estimation
• Optimization and Inverse Problems
• Machine Learning, Deep Learning, Neural Networks.

Some posters on our research activities:

• year 2021: [1], [2], [3], [4]  RAMSES
• year 2020: [1], [2], [3], [4], [5]  SAMM 2020 
• year 2019: [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]
• year 2018: [1], [2], [3], [4], [5], [6]  MoRePaS 2018
• year 2017: [1], [2], [3], [4], [5], [6]  QUIET 2017
• year 2016: [1], [2], [3]  IHP 2016
• year 2015: [1], [2], [3]  MoRePaS 2015
• year 2013: [1]
• year 2012: [1], [2], [3], [4], [5], [6], [7], [8]
• year 2010: [1], [2]

Some of our talks at WCCM 2020 jointly organised with ECCOMAS:

• “Reduced Order Methods for Optimal Flow Control Problems: from time-dependency to nonlinearity“, M. Strazzullo
• “Reduced order modelling in bifurcating parametrised non-linear equations“, F. Pichi
• “Shape Parameterization and Model Reduction Applications for Naval Hydrodynamics” A. Mola
• “Reduced Order Methods for Optimal Flow Control Problems: from time-dependency to nonlinearity“, M. Tezzele
• “Reduced Order Models for Fluid-Structure Interaction: monolithic and partitioned approachs“, M. Nonino
• “A Modular and Data-Driven Framework Involving Reducing Order Methods for Naval Optimization Industrial Problems“, N.Demo

Books

• Benner P., Schilders W., Grivet-Talocia S., Quarteroni A., Rozza G., & Miguel Silveira L., Model Order Reduction: Vol 1-3, Editors: Benner P. , Grivet-Talocia S., Quarteroni A., Rozza G., Schilders W., Miguel Silveira L., De Gruyter, 2020-2021
• Barth, T. J., Griebel, M., Keyes, D. E., Nieminen, R. M., Roose, D., & Schlick, T. Quantification of Uncertainty: Improving Efficiency and Technology, Editors: D’Elia M., Gunzberg M., Rozza G., Springer, 2017
• Boffi, D., Pavarino, L.F., Rozza, G., Scacchi, S., Vergara, C, Mathematical and Numerical Modeling of the Cardiovascular System and Applications. SEMA SIMAI Springer series, Springer, 2018
• van Brummelen, E.H., Corsini, A., Perotto, S., Rozza, G. Numerical Methods for Flows: Fef 2017 Selected Contributions, Springer, 2020
• P. Benner, M. Ohlberger, A. Patera, G. Rozza, K. Urban, Model Reduction of Parametrized Systems. MS&A series, Vol.17, Springer, 2017
• J. S. Hesthaven, G. Rozza, B. Stamm. Certified Reduced Basis Methods for Parametrized Partial Differential Equations. SpringerBriefs in Mathematics. Springer, 2015
•  A. Quarteroni and G. Rozza Eds, Reduced Order Methods for Modeling and Computational Reduction, MS&A series, Vol.9, Springer, 2013
•  D. Ambrosi, A. Quarteroni and G. Rozza Eds, Modelling of Physiological Flows, MS&A series, Vol.5, Springer, 2011.
• Reduced Basis Book: A.T. Patera and G. Rozza, Reduced Basis Approximation and A-Posteriori Error Estimation for Parametrized PDEs, MIT-Pappalardo Graduate Monographs in Mechanical Engineering, (c) MIT, 2007 with Reduced Basis Software Library: rbMIT Software, (c) MIT 2006-15, Tecnology Licensing Office (case 12600); with COMSOL interface for rbMIT.

Past Phd Thesis

• Maria Strazzullo’s Phd thesis, Phd student at SISSA (2021).
• Marco Tezzele’s Phd thesis, Phd student at SISSA (2021).
• Matteo Zancanaro’s Phd thesis, Phd student at SISSA (2021).
• Saddam Hijazi’s Phd thesis, Phd student at SISSA (2020).
• Monica Nonino’s Phd thesis, Phd student at SISSA (2020).
• Federico Pichi’s Phd thesis, Phd student at SISSA (2020).
• Zakia Zainib’s Phd thesis, Phd student at SISSA (2019).
• Shafqat Ali’s Phd thesis, Phd student at SISSA (2018).

• Stefano Lorenzi’s Phd thesis, Phd student at Politecnico di Milano under my co-supervision (2016) [collaboration with Energy Dpt].
• Federico Negri’s Phd thesis, Phd student at EPFL under my co-supervision (2015).
• Francesco Ballarin’s Phd thesis, Phd student at Politecnico di Milano under my co-supervision (2015) [collaboration with MOX]
• Alberto Sartori’s Phd thesis, Phd student at Politecnico di Milano under my co-supervision (2015) [collaboration with Energy Dpt].
• Peng Chen’s PhD thesis, Phd student at EPFL under my co-supervision (2014).
• Andrea Manzoni’s PhD thesis, Phd student at EPFL under my co-supervision (2012).
• Laura Iapichino’s PhD thesis, Phd student at EPFL under my co-supervision (2012).

Past Master Thesis

• Fabio Zoccolan, Stabilized Reduced Order Methods for Advection-Diffusion Optimal Control Problems with random inputs [link] (2021, University of Trieste, Italy)
• Angelo Cetrangolo, Reduced order methods for inverse problem in CFD [link] (2021, Politecnico di Torino, Italy)
• Moaad Khamlich, Model order reduction for bifurcating phenomena in fluid-structure interaction problem. [link] (2021, Politecnico di Milano, Italy)
• Eleonora Donadini, A data-driven approach for time-dependent optimal control problems by dynamic mode decomposition. (2021, University of Trieste, Italy)
• Davide Papapicco, A neural network framework for reduced order modelling of non-linear hyperbolic equations in computational fluid dynamics. [link] (2021, Politecnico di Torino, Italy)
• Anna Ivagnes, Data Enhanced Reduced Order Methods for Turbulent Flows. [link] (2021, Politecnico di Torino, Italy)
• Pierfrancesco Siena, A machine learning-based reduced order model for the investigation of the blood flow patterns in presence of a stenosis of the left main coronary artery.  [link] (2021, Politecnico di Torino, Italy)
• Francesco Romor, Reduction in Parameter Space for Problems approximatedby Discontinuous-Galerkin Method in Computational FluidDynamics (2019, University of Trieste, Italy)
• Julien Genovese, Reduced Order Methods for Uncertainty Quantification in Computational Fluid Dynamics [link] (2019, Politecnico di Torino, Italy)
• Giulio Ortali, A Data-Driven Reduced Order Optimization Approach for Cruise Ship Design [link] (2019, Politecnico di Torino, Italy)
• Giuseppe Infantino, (2019, Politecnico di Torino, Italy).
  
• Moreno Pintore, Efficient Computation of Bifurcation Diagrams with Spectral Element Method and Reduced Order Models [link] (2019, Politecnico di Torino, Italy).
• Fabrizio Garotta, 2018, University of Pavia, Italy.
• Nirav Shah, Finite Element Reduced Basis (Proper Orthogonal Decomposition) Approach for Geometrically Parametrized Stokes Flow (2018, University of Stuttgart, Germany).
• Giulia Meglioli,Comparison of model order reduction approaches in parametrized optimal control problems [link] (2017, Mathematical Engineering at Politecnico di Milano, Milan Italy).
  
• Giacomo Zuccarino, A Reduced order Variational Multiscale Method for parametric flows (2017, University of Trieste, Italy).
• Matteo Zancanaro, Hierarchical model reduction techniques for flows in parametric setting [link] (2017, Aerospace Engineering at Politecnico di Milano, Milan Italy).
• Maria Strazzullo, Reduced Order Methods for Optimal Control Problems: Application in Environmental Marine Sciences and Engineering [link] (2017, University of Trieste, Italy).
  
• Federico Pichi, Reduced order methods for parametric Von Karman equations in nonlinear structural mechanics [link] (2016, University of Rome, Italy).
• Saddam Hijazi, POD-Galerkin Reduced Order Model for the simulation of laminar and turbulent flows around a circular cylinder [link] (2016, University of L’Aquila, Italy).
• Luca Venturi, Weighted reduced order methods for parametrized PDEs in uncertainty quantification problems [link] (2016, University of Trieste, Italy).
• Davide Torlo, Stabilized reduced basis method for transport PDEs with random inputs [link] (2016, University of Trieste, Italy).
• Luca Valsecchi, Reduced order methods for PDEs : a comparison between proper orthogonal decomposition and proper generalized decomposition  [link] (2016, Aerospace Engineering at Politecnico di Milano, Milan Italy).
• Alessandro D’Amario, A reduced-order inverse distance weighting technique for the efficient mesh-motion of deformable interfaces and moving shapes in computational problems [link] (2016, Aerospace Engineering at Politecnico di Milano, Milan Italy).
• Emiliano Cangemi, A POD reduced order method for parameterized Maxwell’s equations and applications [link] (2016, Aerospace Engineering at Politecnico di Milano, Milan Italy).
• Davide Forti, Comparison of Shape Parametrization Techniques for Fluid-Structure Interaction Problems [link] (2012, Aerospace Engineering at Politecnico di Milano).
• Paolo Pacciarini, Stabilized reduced basis method for parametrized advection-diffusion PDEs [link] (2012, Mathematics at University of Pavia).
• Federico Negri, Reduced basis method for parametrized optimal control problems governed by PDEs [link], (2011, Mathematical Engineering at Politecnico di Milano).Anwar Koshakji, Free Form Deformation Techniques for 3D Shape Optimization Problems [link] (2011, Aerospace Engineering at Politecnico di Milano).

• Alberto Trezzini, Reduced Basis Method for 3D problems governed by parametrized PDEs and applications [link] (2010, Aerospace Engineering at Politecnico di Milano).
• Fabrizio Gelsomino, Exploration and comparison of reduced order modelling techniques [link] (2009, Mathematical Engineering, EPFL).
• Claudia Gunther, Optimization of racing car components using reduced basis (potential, thermal and Stokes flow) [link] (2008, Applied Math, Aachen University).
• Roberto Milani, Reduced Basis and Optimization in Linear Elasticity [link] (2006, Aerospace Engineering at Politecnico di Milano)
• Annalisa Quaini, Optimal Control and Reduced Basis Techniques [link] (2005, Aerospace Engineering at Politecnico di Milano).
• Luca Dede’, Optimal Control and Mesh Adaptivity [link] (2004, Aerospace Engineering at Politecnico di Milano).

Past Projects (before 2014)

• Co-applicant in the SNSF (Swiss National Science Foundation) Projects:
  – Reduced basis methods for the optimization of complex systems (2009-2011, 2×36 months Phd Students);
  – Numerical simulation of a sailing boat: dynamics and shape optimization (2011-2013, 24 months Phd Student);
  – Model reduction strategies for control, optimization and uncertainty quantification of parametrized systems (2012-2015, 48 months Phd student.)
• Task Coordinator in the ERC-MathCard Project: Mathematical Modelling and Simulation of the Cardiovascular System, task on Efficient Methods for Control and Optimization (2009-2013).
• Real-Time Reliable Continuum Mechanics Computations: Parametrized PDEs and A-Posteriori Error Estimation for elliptic and parabolic, coercive and non-coercive problems with affine and non-affine parametrization (2006-2010) at MIT. Examples of worked problems.
• Collaborator for aerodynamic modeling and optimization for feasibility study of Solar Impulse Project (2003-2007) at EPFL.

More info about me on:

[math genealogy]