34 citations · 42 across the 21 of their papers we have counts for
7 papers · 1 filter
A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
Giovanni Ziarelli, Stefano Pagani, Nicola Parolini +2
In this work, we aim to formalize a novel scientific machine learning framework to reconstruct the hidden dynamics of the transmission rate, whose inaccurate extrapolation can sign…
Design of a checkerboard counterflow heat exchanger for industrial applications
N. Parolini, V. Covello, A. della Rocca +1
This work is devoted to the design of a checkerboard air-gas heat exchanger suitable for industrial applications. The design of the heat exchanger is optimized in order to obtain t…
An Immersed Boundary Method for Polymeric Continuous Mixing
G. Negrini, N. Parolini, M. Verani
We introduce a new implementation of the Immersed Boundary method in the finite-volume library OpenFOAM. The implementation is tailored to the simulation of temperature-dependent n…
A posteriori error analysis for a coupled Stokes-poroelastic system with multiple compartments
Ivan Fumagalli, Nicola Parolini, Marco Verani
The discretization of fluid-poromechanics systems is typically highly demanding in terms of computational effort. This is particularly true for models of multiphysics flows in the…
Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control
Giovanni Ziarelli, Nicola Parolini, Marco Verani
Since infectious pathogens start spreading into a susceptible population, mathematical models can provide policy makers with reliable forecasts and scenario analyses, which can be…
A Virtual Element method for non-Newtonian fluid flows
P. F. Antonietti, L. Beirao da Veiga, M. Botti +2
In this paper, we design and analyze a Virtual Element discretization for the steady motion of non-Newtonian, incompressible fluids. A specific stabilization, tailored to mimic the…
Structure-preserving neural networks in data-driven rheological models
Nicola Parolini, Andrea Poiatti, Julian Vene' +1
In this paper we address the importance and the impact of employing structure preserving neural networks as surrogate of the analytical physics-based models typically employed to d…