Andrea Manzoni

Assistant Professor (RTDb)
Assistant Professor

Phone:+39 02 2399 4638
Fax: +39 02 2399
Office: 14-Nave - VI floor

  • Available MOX Reports
  • Theses
  • Thesis Proposals
  • MOX Projects

Fresca, S.; Manzoni, A.; Dedè, L.; Quarteroni, A.
Deep learning-based reduced order models in cardiac electrophysiology

Massi, M.c., Gasperoni, F., Ieva, F., Paganoni, A.m., Zunino, P., Manzoni, A., Franco, N.r., Et Al.
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort

Fresca, S.; Dede', L.; Manzoni, A.
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

Manzoni, A; Quarteroni, A.; Salsa, S.
A saddle point approach to an optimal boundary control problem for steady Navier-Stokes equations

Dal Santo, N.; Manzoni, A.
Hyper-reduced order models for parametrized unsteady Navier-Stokes equations on domains with variable shape

Pagani, S.; Manzoni, A.; Carlberg, K.
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method

Dal Santo, N.; Deparis, S.; Manzoni, A.; Quarteroni, A.
Multi space reduced basis preconditioners for parametrized Stokes equations

Dal Santo, N.; Deparis, S.; Manzoni, A.; Quarteroni, A.
An algebraic least squares reduced basis method for the solution of nonaffinely parametrized Stokes equations

Manzoni, A; Bonomi, D.; Quarteroni, A.
Reduced order modeling for cardiac electrophysiology and mechanics: new methodologies, challenges & perspectives

Dal Santo, N.; Deparis, S.; Manzoni, A.; Quarteroni, A.
Multi space reduced basis preconditioners for large-scale parametrized PDEs

Pagani, S.; Manzoni, A.; Quarteroni, A.
Numerical approximation of parametrized problems in cardiac electrophysiology by a local reduced basis method

Quarteroni, A.; Manzoni, A.; Vergara, C.
The Cardiovascular System: Mathematical Modeling, Numerical Algorithms, Clinical Applications

Pagani, S.; Manzoni, A.; Quarteroni, A.
A Reduced Basis Ensemble Kalman Filter for State/parameter Identification in Large-scale Nonlinear Dynamical Systems

Bonomi, D.; Manzoni, A.; Quarteroni, A.
A matrix discrete empirical interpolation method for the efficient model reduction of parametrized nonlinear PDEs: application to nonlinear elasticity problems

Ballarin, F.; Faggiano, E.; Ippolito, S.; Manzoni, A.; Quarteroni, A.; Rozza, G.; Scrofani, R.
Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a PODGalerkin method and a vascular shape parametrization

Manzoni, A.; Pagani, S.
A certified reduced basis method for PDE-constrained parametric optimization problems by an adjoint-based approach

Fumagalli, I.; Manzoni, A.; Parolini, N.; Verani, M.
Reduced basis approximation and a posteriori error estimates for parametrized elliptic eigenvalue problems

Manzoni, A.; Pagani, S.; Lassila, T.
Accurate solution of Bayesian inverse uncertainty quantification problems using model and error reduction methods

Ballarin, F.; Manzoni, A.; Quarteroni, A.; Rozza, G.
Supremizer stabilization of POD-Galerkin approximation of parametrized Navier-Stokes equations

Lassila, T.; Manzoni, A.; Quarteroni, A.; Rozza, G.
Model order reduction in fluid dynamics: challenges and perspectives

Negri, F.; Rozza, G.; Manzoni, A.; Quarteroni, A.
Reduced basis method for parametrized elliptic optimal control problems

Lassila, T.; Manzoni, A.; Quarteroni, A.; Rozza, G.
Generalized reduced basis methods and n width estimates for the approximation of the solution manifold of parametric PDEs

Manzoni, A.; Quarteroni, A.; Gianluigi Rozza, G.
Computational reduction for parametrized PDEs: strategies and applications

Lassila, T.; Manzoni, A.; Quarteroni, A.; Rozza, G.
Boundary control and shape optimization for the robust design of bypass anastomoses under uncertainty

Lassila, T.; Manzoni, A.; Quarteroni, A.; Rozza, G.
A reduced computational and geometrical framework for inverse problems in haemodynamics

Manzoni, A.; Quarteroni, A.; Rozza, G.
Model reduction techniques for fast blood flow simulation in parametrized geometries

Quarteroni, A.; Rozza, G.; Manzoni, A.
Certified Reduced Basis Approximation for Parametrized Partial Differential Equations and Applications

Manzoni, Andrea; Quarteroni, Alfio; Rozza, Gianluigi
Shape optimization for viscous flows by reduced basis methods and free-form deformation

Forward Uncertainty Quantification for Dynamical Systems

Uncertainty quantification (UQ) can be defined as the science of identifying, quantifying, and reducing uncertainties associated with models, numerical algorithms, experiments, and predicted outcomes or quantities of interest. The thesis project aims at investigating new approaches to perform uncertainty propagation (from model inputs to time-dependent output quantities of interest) for nonlinear dynamical systems. Both sensitivity analysis and uncertainty propagation will be addressed, exploiting Monte Carlo techniques and suitable extensions (multi-level Monte Carlo techniques); numerical accuracy of the estimated quantities will be carefully assessed. As motivating example, we will start from recently proposed generalized SEIR models to describe the Covid-19 pandemic. This project is in collaboration with Dr. Lorenzo Tamellini (CNR-IMATI, Pavia).

Neural network augmented inverse problems for PDEs

Inverse problems for PDEs such as parameter estimation can be cast in the form of optimal control (or PDE-constrained optimization) problems, requiring suitable regularization strategies to mitigate their ill-posedness. The thesis project aims at investigating a recently proposed approach involving neural networks, where these latter act as a prior for the coefficient to be estimated from noisy data, and applying it to inverse problems arising for instance in cardiac electrophysiology or computational fluid dynamics.

Iheart,Eu Erc Advanced Grant
An Integrated Heart Model for the simulation of the cardiac function. The goal of the iHEART project is to construct, mathematically analyze, numerically approximate, computationally solve, and validate on clinically relevant cases a mathematically-based integrated heart model for the human cardiac function. The integrated model comprises several core cardiac models - electrophysiology, solid and fluid mechanics, microscopic cellular force generation, and valve dynamics - which are then coupled and finally embedded into the systemic and pulmonary blood circulations. It is a multiscale system of Partial Differential Equations and Ordinary Differential Equations featuring multiphysics interactions among the core models.