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20182025
most citedDeepBND: a Machine Learning approach to enhance Multiscale Solid Mechanics

1 citations · 1 across the 2 of their papers we have counts for

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math.NA2025

Model order reduction of hemodynamics by space-time reduced basis and reduced fluid-structure interaction

Riccardo Tenderini, Simone Deparis

In this work, we apply the space-time Galerkin reduced basis (ST-GRB) method to a reduced fluid-structure interaction model, for the numerical simulation of hemodynamics in arterie…

math.NA2025

A spline-based hexahedral mesh generator for patient-specific coronary arteries

Fabio Marcinnó, Jochen Hinz, Annalisa Buffa +1

This paper presents a spline-based hexahedral mesh generator for tubular geometries commonly encountered in haemodynamics studies, in particular coronary arteries. We focus on tech…

math.NA20211 cited

DeepBND: a Machine Learning approach to enhance Multiscale Solid Mechanics

Felipe Rocha, Simone Deparis, Pablo Antolin +1

Effective properties of materials with random heterogeneous structures are typically determined by homogenising the mechanical quantity of interest in a window of observation. The…

math.NA2020

Model order reduction of flow based on a modular geometrical approximation of blood vessels

Luca Pegolotti, Martin Pfaller, Alison Marsden +1

We are interested in a reduced order method for the efficient simulation of blood flow in arteries. The blood dynamics is modeled by means of the incompressible Navier-Stokes equat…

math.NA2018

Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space

Simone Deparis, Luca Pegolotti

This work focuses on the development of a non-conforming domain decomposition method for the approximation of PDEs based on weakly imposed transmission conditions: the continuity o…