Luca Pegolotti

TL;DR: This work proposes to accomplish the approximation of partial differential equations with a data-driven approach based on the reduced basis method and machine learning with a neural network embedding a reduced basis solver as exotic activation function in the last layer.

TL;DR: In this article, the effects of the spatial discretization by IGA and the use of different B-spline basis functions on the accuracy of the approximation, in particular regarding the front velocity and the dispersion error, were evaluated.

TL;DR: In this paper, a reduced order method for the efficient simulation of blood flow in arteries is proposed, based on an approximated domain-decomposition of the target geometry into a number of subdomains obtained from the parametrized deformation of geometrical building blocks (e.g., straight tubes and model bifurcations).

TL;DR: This work derives the correct essential boundary conditions to impose at each stage in order to retain the convergence order of the Rosenbrock schemes and considers two benchmark tests: a flow problem with imposed oscillatory pressure gradient whose analytical solution is known and the classical flow past a cylinder.

TL;DR: In this paper , the authors describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments.