| Data driven approximation of parametrized PDEs by reduced basis and neural networks N Dal Santo, S Deparis, L Pegolotti Journal of Computational Physics 416, 109550, 2020 | 63 | 2020 |
| Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow MR Pfaller, J Pham, A Verma, L Pegolotti, NM Wilson, DW Parker, W Yang, ... International journal for numerical methods in biomedical engineering 38 (10 …, 2022 | 42 | 2022 |
| Isogeometric analysis of the electrophysiology in the human heart: numerical simulation of the bidomain equations on the atria L Pegolotti, L Dedè, A Quarteroni Computer Methods in Applied Mechanics and Engineering 343, 52-73, 2019 | 38 | 2019 |
| Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease EL Schwarz, L Pegolotti, MR Pfaller, AL Marsden Biophysics Reviews 4 (1), 2023 | 29 | 2023 |
| Model order reduction of flow based on a modular geometrical approximation of blood vessels L Pegolotti, MR Pfaller, AL Marsden, S Deparis Computer methods in applied mechanics and engineering 380, 113762, 2021 | 27 | 2021 |
| Learning reduced-order models for cardiovascular simulations with graph neural networks L Pegolotti, MR Pfaller, NL Rubio, K Ding, RB Brufau, E Darve, ... Computers in Biology and Medicine 168, 107676, 2024 | 19 | 2024 |
| Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space S Deparis, A Iubatti, L Pegolotti ESAIM: Mathematical Modelling and Numerical Analysis 53 (5), 1667-1694, 2019 | 7 | 2019 |
| Application of the Rosenbrock methods to the solution of unsteady 3D incompressible Navier-Stokes equations S Deparis, MO Deville, F Menghini, L Pegolotti, A Quarteroni Computers & Fluids 179, 112-122, 2019 | 6 | 2019 |
| 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 Frontiers in physiology 11, 1086, 2020 | 4 | 2020 |
| Deep neural network to accurately predict left ventricular systolic function under mechanical assistance J Bonnemain, M Zeller, L Pegolotti, S Deparis, L Liaudet Frontiers in Cardiovascular Medicine 8, 752088, 2021 | 2 | 2021 |
| Isogeometric Analysis of cardiac electrophysiology: application to the human atria L Pegolotti | 2 | 2017 |
| Bayesian Windkessel calibration using optimized 0D surrogate models J Richter, J Nitzler, L Pegolotti, K Menon, J Biehler, WA Wall, ... arXiv preprint arXiv:2404.14187, 2024 | | 2024 |
| Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences NL Rubio, L Pegolotti, MR Pfaller, EF Darve, AL Marsden arXiv preprint arXiv:2402.15651, 2024 | | 2024 |
| Reduced-order modeling of cardiovascular hemodynamics MR Pfaller, L Pegolotti, J Pham, NL Rubio, AL Marsden Biomechanics of the Aorta, 449-476, 2024 | | 2024 |
| Data-Driven Modeling of Pressure Differentials over Vascular Junctions N Rubio, L Pegolotti, M Pfaller, J Pham, E Darve, A Marsden Bulletin of the American Physical Society, 2023 | | 2023 |
| Reduction techniques for PDEs built upon Reduced Basis and Domain Decomposition Methods with applications to hemodynamics L Pegolotti EPFL, 2021 | | 2021 |
Alison Lesley MarsdenDouglass M. and Nola Leishman Professor of Cardiovascular Disease, Pediatrics and Bioengineering在 stanford.edu 的电子邮件经过验证
