Publications by Simone Deparis

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

Simone Deparis, Annalisa Buffa, Jochen Peter Hinz

This paper presents a spline-based hexahedral mesh generator for tubular geometries commonly encountered in hæmodynamics studies, in particular coronary arteries. We focus on techniques for accurately meshing vessels with stenoses and aneurysms, as well as ...

2025

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Simone Deparis, Riccardo Tenderini, Nicholas Mueller

In this work, we analyze space-time reduced basis methods for the efficient numerical simulation of haemodynamics in arteries. The classical formulation of the reduced basis (RB) method features dimensionality reduction in space, while finite difference sc ...

Siam Publications2024

EQUITY AND EXAMINATION TIME PRESSURE IN FIRST YEAR MATHEMATICS FOR ENGINEERS

Simone Deparis, Roland John Tormey, Cécile Hardebolle, Alexandra Corina Niculescu

The 'gender mathematics gap' which persists in many countries means that women students may, on average, have less high school preparation in mathematics than men students entering engineering education. This in turn could impact their performance in first ...

European Society for Engineering Education (SEFI)2024

Reluctance Despite Recognition: Student Perceptions of Benefits of Group Work

Kathryn Hess Bellwald, Simone Deparis, Joelyn de Lima

As engineering educators pivot towards implementing evidence-based pedagogical strategies, group work - or collaborative work - is being increasingly used in undergraduate classrooms. In mathematics, collaboration is a crucial disciplinary practice. Partic ...

European Society for Engineering Education (SEFI)2024

Can Knowledge Transfer Techniques Compensate for the Limited Myocardial Infarction Data by Leveraging Haemodynamics? An in silico Study

Emmanuel Abbé, Simone Deparis, Annalisa Buffa, Riccardo Tenderini, Ortal Yona Senouf

The goal of this work is to investigate the ability of transfer learning (TL) and multitask learning (MTL) algorithms to predict tasks related to myocardial infarction (MI) in a small-data regime, leveraging a larger dataset of haemodynamic targets. The da ...

Springer Nature2023

DeepBND: A machine learning approach to enhance multiscale solid mechanics

Simone Deparis, Annalisa Buffa, Pablo Antolin Sanchez, Felipe Figueredo Rocha

Effective properties of materials with random heterogeneous structures are typically determined by homogenising the mechanical quantity of interest in a window of observation. The entire problem setting encompasses the solution of a local PDE and some aver ...

2023

Conservation of Forces and Total Work at the Interface Using the Internodes Method

Simone Deparis

The Internodes method is a general purpose method to deal with non-conforming discretizations of partial differential equations on 2D and 3D regions partitioned into disjoint subdomains. In this paper we are interested in measuring how much the Internodes ...

2022

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Alfio Quarteroni, Simone Deparis, Stefano Pagani, Riccardo Tenderini

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by ...

2022

The INTERNODES method for applications in contact mechanics and dedicated preconditioning techniques

Simone Deparis, Guillaume Anciaux, Yannis Dirk Voet

The mortar finite element method is a well-established method for the numerical solution of partial differential equations on domains displaying non-conforming interfaces. The method is known for its application in computational contact mechanics. However, ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Gender, prior knowledge, and the impact of a flipped linear algebra course for engineers over multiple years

Simone Deparis, Himanshu Verma, Roland John Tormey, Cécile Hardebolle

Background Research shows that active pedagogies could play an important role in achieving more equitable outcomes for diverse groups of students in Science, Technology, Engineering, and Mathematics (STEM). Although flipped classes are a popular active met ...

American Society for Engineering Education2022

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

Simone Deparis, Luca Pegolotti

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 equations. Our algorithm is based on an approximated domain-decomposition of the ...

2021

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance

Simone Deparis, Luca Pegolotti

Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters o ...

FRONTIERS MEDIA SA2021

Analysis of morphological and haemodynamical indexes in abdominal aortic aneurysms as preliminary indicators of intraluminal thrombus deposition

Alfio Quarteroni, Simone Deparis, Claudia Maria Colciago

In this paper we study the correlation between the Wall Shear Stress (WSS), a haemodynamical index derived from numerical simulations, and an new index MFA-ILT for the characterisation of intraluminal thrombus (ILT) in presence of abdominal aortic aneurysm ...

2020

Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals

Simone Deparis, Jean Bonnemain, Luca Pegolotti

The evaluation of cardiac contractility by the assessment of the ventricular systolic elastance function is clinically challenging and cannot be easily obtained at the bedside. In this work, we present a framework characterizing left ventricular systolic f ...

2020

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

Simone Deparis, Luca Pegolotti, Niccolò Dal Santo

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

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

Simone Deparis, Luca Pegolotti, Antonio Iubatti

This work focuses on the development of a non-conforming method for the coupling of PDEs based on weakly imposed transmission conditions: the continuity of the global solution is enforced by a finite number of Lagrange multipliers defined over the interfac ...

2019

Multi space reduced basis preconditioners for parametrized Stokes equations

Alfio Quarteroni, Simone Deparis, Andrea Manzoni, Niccolò Dal Santo

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 ...

PERGAMON-ELSEVIER SCIENCE LTD2019

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

Alfio Quarteroni, Simone Deparis, Andrea Manzoni, Niccolò Dal Santo

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 ...

ELSEVIER SCIENCE SA2019

The LifeV library: engineering mathematics beyond the proof of concept

Luca Formaggia, Simone Deparis, Davide Forti

LifeV is a library for the finite element (FE) solution of partial differential equations in one, two, and three dimensions. It is written in C++ and designed to run on diverse parallel architectures, including cloud and high performance computing faciliti ...

2018

Multi space reduced basis preconditioners for large-scale parametrized PDEs

Alfio Quarteroni, Simone Deparis, Andrea Manzoni, Niccolò Dal Santo

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 ...

2018