Bio


Luca Pegolotti is a Postdoc in the Cardiovascular Biomechanics Computation Lab led by Prof. Alison Marsden. He is interested in data-driven model order reduction techniques for cardiovascular simulations. His areas of expertise include scientific computing, high-performance computing, and deep learning.

Luca Pegolotti completed a BCs in Mathematical Engineering at Politecnico di Milano in 2014 and a MSc in Computational Science and Engineering at the École polytechnique fédérale de Lausanne (EPFL) in 2017. He graduated with a PhD in Mathematics at EPFL in 2020 under the supervision of his PhD advisor, Prof. Simone Deparis. In his thesis, "Reduction techniques for PDEs built upon Reduced Basis and Domain Decomposition Methods with applications to hemodynamics", he focuses on projection-based model order reduction methods for cardiovascular flow.

Stanford Advisors


Research Interests


  • Data Sciences
  • Research Methods

All Publications


  • Automated generation of 0D and 1D reduced-order models of patient-specific blood flow. International journal for numerical methods in biomedical engineering Pfaller, M. R., Pham, J., Verma, A., Pegolotti, L., Wilson, N. M., Parker, D. W., Yang, W., Marsden, A. L. 2022: e3639

    Abstract

    Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N=72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/cnm.3639

    View details for PubMedID 35875875

  • Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance FRONTIERS IN CARDIOVASCULAR MEDICINE Bonnemain, J., Zeller, M., Pegolotti, L., Deparis, S., Liaudet, L. 2021; 8: 752088

    Abstract

    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 of LV systolic function. DNN input data were systemic and pulmonary arterial pressure signals, and rotation speeds of the device. Output data were parameters of LV systolic function, including end-systolic maximal elastance (E max,lv ), a variable essential for adequate hemodynamic assessment of the LV. A 0D model of the cardiovascular system, including a wide range of LVAD settings and incorporating the whole spectrum of heart failure, was used to generate data for the training procedure of the DNN. The DNN predicted E max,lv with a mean relative error of 10.1%, and all other parameters of LV function with a mean relative error of <13%. The framework was then able to retrieve a number of LV physiological variables (i.e., pressures, volumes, and ejection fraction) with a mean relative error of <5%. Our method provides an innovative tool to assess LV hemodynamics under device assistance, which could be helpful for a better understanding of LV-LVAD interactions, and for therapeutic optimization.

    View details for DOI 10.3389/fcvm.2021.752088

    View details for Web of Science ID 000726202100001

    View details for PubMedID 34765658

    View details for PubMedCentralID PMC8576185

  • Model order reduction of flow based on a modular geometrical approximation of blood vessels. Computer methods in applied mechanics and engineering Pegolotti, L., Pfaller, M. R., Marsden, A. L., Deparis, S. 2021; 380

    Abstract

    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 target geometry into a number of subdomains obtained from the parametrized deformation of geometrical building blocks (e.g., straight tubes and model bifurcations). On each of these building blocks, we build a set of spectral functions by Proper Orthogonal Decomposition of a large number of snapshots of finite element solutions (offline phase). The global solution of the Navier-Stokes equations on a target geometry is then found by coupling linear combinations of these local basis functions by means of spectral Lagrange multipliers (online phase). Being that the number of reduced degrees of freedom is considerably smaller than their finite element counterpart, this approach allows us to significantly decrease the size of the linear system to be solved in each iteration of the Newton-Raphson algorithm. We achieve large speedups with respect to the full order simulation (in our numerical experiments, the gain is at least of one order of magnitude and grows inversely with respect to the reduced basis size), whilst still retaining satisfactory accuracy for most cardiovascular simulations.

    View details for DOI 10.1016/j.cma.2021.113762

    View details for PubMedID 34176992

  • Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals FRONTIERS IN PHYSIOLOGY Bonnemain, J., Pegolotti, L., Liaudet, L., Deparis, S. 2020; 11: 1086

    Abstract

    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 function from clinically readily available data, including systemic and pulmonary arterial pressure signals. We implemented and calibrated a deep neural network (DNN) consisting of a multi-layer perceptron with 4 fully connected hidden layers and with 16 neurons per layer, which was trained with data obtained from a lumped model of the cardiovascular system modeling different levels of cardiac function. The lumped model included a function of circulatory autoregulation from carotid baroreceptors in pulsatile conditions. Inputs for the DNN were systemic and pulmonary arterial pressure curves. Outputs from the DNN were parameters of the lumped model characterizing left ventricular systolic function, especially end-systolic elastance. The DNN adequately performed and accurately recovered the relevant hemodynamic parameters with a mean relative error of less than 2%. Therefore, our framework can easily provide complex physiological parameters of cardiac contractility, which could lead to the development of invaluable tools for the clinical evaluation of patients with severe cardiac dysfunction.

    View details for DOI 10.3389/fphys.2020.01086

    View details for Web of Science ID 000575951400001

    View details for PubMedID 33071803

    View details for PubMedCentralID PMC7533610

  • Data driven approximation of parametrized PDEs by reduced basis and neural networks JOURNAL OF COMPUTATIONAL PHYSICS Dal Santo, N., Deparis, S., Pegolotti, L. 2020; 416
  • Application of the Rosenbrock methods to the solution of unsteady 3D incompressible Navier-Stokes equations COMPUTERS & FLUIDS Deparis, S., Deville, M. O., Menghini, F., Pegolotti, L., Quarteroni, A. 2019; 179: 112-122
  • Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space ESAIM: M2AN Deparis, S., Iubatti, A., Pegolotti, L. 2019; 53 (5): 1667 - 1694

    View details for DOI 10.1051.m2an/2019030

  • Isogeometric Analysis of the electrophysiology in the human heart: Numerical simulation of the bidomain equations on the atria COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING Pegolotti, L., Dede, L., Quarteroni, A. 2019; 343: 52-73