Research Interests and Activities

Group activities in a glance


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.


Some posters on our research activities:


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


Books


Past Phd Thesis


Past Master Thesis

  • 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)
  • 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)

More info about me on:

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