Publications by Niccolò Dal Santo

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The multiquery solution of parametric partial differential equations (PDEs), that is, PDEs depending on a vector of parameters, is computationally challenging and appears in several engineering contexts, such as PDE-constrained optimization, uncertainty qu ...

In this paper we propose a new, purely algebraic, Petrov-Galerkin reduced basis (RB) method to solve the parametrized Stokes equations, where parameters serve to identify the (variable) domain geometry. Our method is obtained as an algebraic least squares ...

The aim of this work is to propose and analyse a new high-order discontinuous Galerkin finite element method for the time integration of a Cauchy problem for second-order ordinary differential equations. These equations typically arise after space semidisc ...

In this work, we set up a new, general, and computationally efficient way to tackle parametrized fluid flows modeled through unsteady Navier-Stokes equations defined on domains with variable shape, when relying on the reduced basis method. We easily descri ...

We introduce a two-level preconditioner for the efficient solution of large scale saddle point linear systems arising from the finite element (FE) discretization of parametrized Stokes equations. This preconditioner extends the Multi Space Reduced Basis (M ...

In this work we introduce a new two-level preconditioner for the efficient solution of large-scale linear systems arising from the discretization of parametrized PDEs. The proposed preconditioner combines in a multiplicative way a reduced basis solver, whi ...

Niccolò Dal Santo

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Multi space reduced basis preconditioners for parametrized partial differential equations

An algebraic least squares reduced basis method for the solution of nonaffinely parametrized Stokes equations

A high-order discontinuous Galerkin approximation to ordinary differential equations with applications to elastodynamics

Hyper-reduced order models for parametrized unsteady Navier-Stokes equations on domains with variable shape

Multi space reduced basis preconditioners for parametrized Stokes equations

Multi space reduced basis preconditioners for large-scale parametrized PDEs

Data driven approximation of parametrized PDEs by reduced basis and neural networks