[1] Model order reduction of hæmodynamics by space–time reduced basis and reduced fluid–structure interaction

R. Tenderini; S. Deparis

DOI : 10.1016/j.cma.2025.118347

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

F. Marcinnò; J. Hinz; A. Buffa; S. Deparis

DOI : 10.1016/j.cma.2025.118153

[3] SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

R. Tenderini; N. Mueller; S. Deparis

DOI : 10.1137/22M1509114

[4] DeepBND: A machine learning approach to enhance multiscale solid mechanics

F. Rocha; S. Deparis; P. Antolin; A. Buffa

DOI : 10.1016/j.jcp.2023.111996

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

Y. Voet; G. Anciaux; S. Deparis; P. Gervasio

DOI : 10.1016/j.camwa.2022.09.019

[6] PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

R. Tenderini; S. Pagani; A. Quarteroni; S. Deparis

DOI : 10.1137/21M1438529

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

C. Hardebolle; H. Verma; R. Tormey; S. Deparis

DOI : 10.1002/jee.20467

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

S. Deparis; P. Gervasio

DOI : 10.1007/s10013-022-00560-9

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

J. Bonnemain; M. Zeller; L. Pegolotti; S. Deparis; L. Liaudet

DOI : 10.3389/fcvm.2021.752088

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

L. Pegolotti; M. R. Pfaller; A. L. Marsden; S. Deparis

DOI : 10.1016/j.cma.2021.113762

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

J. Bonnemain; L. Pegolotti; L. Liaudet; S. Deparis

DOI : 10.3389/fphys.2020.01086

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

N. Dal Santo; S. Deparis; L. Pegolotti

DOI : 10.1016/j.jcp.2020.109550

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

C. M. Colciago; S. Deparis; M. Domanin; C. Riccobene; E. Schenone  et al.

DOI : 10.1007/s10237-019-01269-4

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

S. Deparis; A. Iubatti; L. Pegolotti

DOI : 10.1051/m2an/2019030

[15] Application of the Rosenbrock methods to the solution of unsteady 3D incompressible Navier-Stokes equations

S. Deparis; M. O. Deville; F. Menghini; L. Pegolotti; A. Quarteroni

DOI : 10.1016/j.compfluid.2018.10.005

[16] Reduced Numerical Approximation of Reduced Fluid-Structure Interaction Problems With Applications in Hemodynamics

C. M. Colciago; S. Deparis

DOI : 10.3389/fams.2018.00018

[17] Multi space reduced basis preconditioners for large-scale parametrized PDEs

N. Dal Santo; S. Deparis; A. Manzoni; A. Quarteroni

DOI : 10.1137/16M1089149

[18] The LifeV library: engineering mathematics beyond the proof of concept

L. Bertagna; S. Deparis; L. Formaggia; D. Forti; A. Veneziani

[19] A parallel algorithm for the solution of large-scale nonconforming fluid-structure interaction problems in hemodynamics

D. Forti; A. Quarteroni; S. Deparis

DOI : 10.4208/jcm.1702-m2016-0630

[20] A Monolithic Approach to Fluid–Composite Structure Interaction

D. Forti; M. Bukac; A. Quaini; S. Canic; S. Deparis

DOI : 10.1007/s10915-017-0363-5