3.1.1 Cardiovascular hemodynamics

We distinguish between cardiac hemodynamics (blood flow inside the four chambers of the heart) and vascular hemodynamics (blood flow in the vessels of the body).

Cardiac hemodynamics. The numerical simulation of cardiac hemodynamics presents many difficulties. We can mention, for instance, the large deformation of the cardiac chambers and the complex fluid-structure interaction (FSI) phenomena between blood, the valves and the myocardium. Blood flow can be described by the incompressible Navier-Stokes equations which have to be coupled with a bio-physical model of the myocardium electro-mechanics and a mechanical model of the valves. The coupling between the fluid and the solid media is enforced by kinematic and dynamic coupling conditions, which guarantee the continuity of velocity and stresses across the interface. In spite of the significant advances achieved since the beginning of this century (see, e.g., 72, 80, 71, 74, 64), the simulation of all the fluid-structure interaction phenomena involved in the heart hemodynamics remains a complex and challenging problem.

Heart valves are definitely a bottleneck of the problem, particularly due to their fast dynamics and the contact phenomena at high pressure-drops. Computational cost is recognized as one of the key difficulties, related to the efficiency of the FSI coupling method and the robustness of the contact algorithm. Furthermore, the numerical discretization of these coupled systems requires to deal with unfitted fluid and solid meshes, which are known to complicate the accuracy and/or the robustness of the numerical approximations (see Section 3.3.2 below).

The ultimate goal of the proposed research activity is the simulation of the complete fluid-structure-contact interaction phenomena involved within the heart. Most of this work will be carried out in close collaboration with the M3DISIM project-team, which has a wide expertise on the modeling, simulation and estimation of myocardium electro-mechanics. We will also consider simplified approaches for cardiac hemodynamics (see, e.g., 44, 59, 62). The objective is to develop mathematically sound models of reduced valve dynamics with the purpose of enhancing the description of the pressure dynamics right after the opening/closing of the valve (traditional models yield spurious pressure oscillations).

Vascular hemodynamics. The modeling and simulation of vascular hemodynamics in large vessels has been one of the core research topics of some members of COMMEDIA, notably as regards the fluid-structure interaction phenomena. Here we propose to investigate the modeling of pathological scenarios, such as the hemorrhage phenomena in smaller vessels. Modeling of hemorrhage is motivated by the medical constatation that, after a primary vessel wall rupture, secondary vessel wall ruptures are observed. Biologists postulate that the mechanical explanation of this phenomena might be in the change of applied stress due to blood bleeding. We propose to model and simulate the underlying coupled system, blood vessel flow through the external tissue, to estimate the effect of the subsequent stress variation.

3.1.2 Respiratory flows

The motivation of the proposed research activities is to develop a hierarchy of easily parametrizable models allowing to describe and efficiently simulate the physical, mechanical and biological phenomena related to human respiration, namely,

ventilation, particle deposition, gas diffusion and coupling with the circulatory system.

Ventilation. The current modeling approaches (either 3D–0D coupled models where the 3D Navier-Stokes equations are solved in truncated geometries of the bronchial tree with appropriate lumped boundary conditions, or 0D–3D coupled models where the lung parenchyma is described by a 3D elastic media irrigated by a simplified bronchial tree) provide satisfactory results in the case of mechanical ventilation or normal breathing. Realistic volume-flow phase portraits can also be simulated in the case of forced expiration (see 46, 56, 77), but the magnitude of the corresponding pressure is not physiological. The current models must be enriched since they do not yet correctly describe all the physiological phenomena at play. We hence propose to extend the 0D–3D (bronchial tree–parenchyma) model developed in the team, by considering a non-linear, viscoelastic and possibly poro-elastic description of the parenchyma with appropriate boundary conditions that describe ribs and adjacent organs and taking into account an appropriate resistive model.

So far, the motion of the trachea and proximal bronchi has been neglected in the ventilation models (see, e.g., 78). These features can be critical for the modeling of pathologic phenomena such as sleep apnea and occlusion of the airways. This would be a long-term goal where fluid-structure interaction and the possible contact phenomena will be taken into account, as in the simulation of cardiac hemodynamics (see Section 3.1.1).

Aerosol and gas diffusion. The dynamics of aerosols in the lung have been widely studied from the mathematical modeling standpoint. They can be described by models at different scales: the microscopic one for which each particle is described individually, the mesoscopic (or kinetic) one for which a density of probability is considered, or the macroscopic one where reaction-diffusion equations describing the behavior of the constituant concentration are considered. The objective of COMMEDIA will mainly be to develop the kinetic approach that allows a precise description of the deposition area at controlled computational costs. Part of this study could be done in collaboration with colleagues from the Research Center for Respiratory Diseases at Inserm Tours (UMR1100).

The macroscopic description is also appropriate for the diffusion of gases (oxygen and carbon dioxide) in the bronchial tree (see 73). Regarding the influence of the carrier gas, if the patient inhales a different mixture of air such as a Helium-Oxygen mixture, the diffusion mechanisms could be modified. In this context, the goal is to evaluate if the cross-diffusion (and thus the carrier gas) modifies the quantities of oxygen diffused. Part of this work will be carried out in collaboration with members of the LJLL and of the MAP5.

As a long term goal, we propose to investigate the coupling of these models to models of diffusion in the blood or to perfusion models of the parenchyma, and thus, have access thanks to numerical simulations to new indices of ventilation efficiency (such as dissolved oxygen levels), depending on the pathology considered or the resting or exercise condition of the patient.

3.2.1 Fluid flow reconstruction from medical imaging

A first problem which is currently under study at COMMEDIA is the reconstruction of the flow state from Doppler ultrasound measurements. This is a cheap and largely available imaging modality where the measure can be interpreted as the average on a voxel of the velocity along the direction of the ultrasound beam. The goal is to perform a full-state estimation in a time compatible with a realistic application.

A second problem which is relevant is the flow and wall dynamics reconstruction using 4D-flow MRI. This imaging modality is richer than Doppler ultrasound and provides directly a measure of the 3D velocity field in the voxels. This enables the use of direct estimation methods at a reduced computational cost with respect to the traditional variational data assimilation approaches. Yet, the sensitivity of the results to subsampling and noise is still not well understood.

We also propose to address the issues related to uncertainty quantification. Indeed, measurements are corrupted by noise and the parameters as well as the available data of the system are either hidden or not known exactly (see 70). This uncertainty makes the estimation difficult and has a large impact on the precision of the reconstruction, to be quantified in order to provide a reliable tool.

3.2.2 Inverse problem in electro-cardiography

The objective of the inverse problem in electro-cardiography is to recover information about the cardiac electrical activity from electrical measurements on the body surface (for instance from electrocardiograms). We propose to investigate approaches based on recent methods for the Cauchy problem reported in 54. Basically, the idea consists in regularizing the discrete inverse problem using stabilized finite element methods, without the need of integrating a priori knowledge of the solution, only regularity on the exact solution is required.

3.2.3 Safety pharmacology

One of the the most important problems in pharmacology is cardio-toxicity (see 69). The objective is to predict whether or not a molecule alters in a significant way the normal functioning of the cardiac cells. This problem can be formulated as inferring the impact of a drug on the ionic currents of each cell based on the measured electrical signal (e.g., electrograms from Micro-Electrodes Arrays). The proposed approach in collaboration with two industrial partners (NOTOCORD and Ncardia) consists in combining available realistic data with virtual ones obtained by numerical simulations. These two datasets can be used to construct efficient classifiers and regressors using machine learning tools (see 51) and hence providing a rapid way to estimate the impact of a molecule on the electrical activity. The methodological aspects of this work are addressed in Section 3.3.3.

3.3.1 Mathematical analysis of PDEs

The mathematical analysis of the multi-scale and multi-physics models are a fundamental tool of the simulation chain. Indeed, well-posedness results provide precious insights on the properties of solutions of the systems which can, for instance, guide the design of the numerical methods or help to discriminate between different modeling options.

Fluid-structure interaction. Most of the existing results concern the existence of solutions locally in time or away from contacts. One fundamental problem, related to the modeling and simulation of valve dynamics (see Sections 3.1.1 and 3.3.2), is the question of whether or not the model allows for contact (see 68, 66). The proposed research activity is aimed at investigating the case of both immersed rigid or elastic structures and explore if the considered model allows for contact and if existence can be proved beyond contact. The question of the choice of the model is crucial and considering different types of fluid (newtonian or non newtonian), structure (smooth or rough, elastic, viscoelastic, poro-elastic), or various interface conditions has an influence on whether the model allows contact or not.

Fluid–structure mixture. The main motivation to study fluid-solid mixtures (i.e., porous media consisting of a skeleton and connecting pores filled with fluid) comes from the modeling of the lung parenchyma and cerebral hemorrhages (see Sections 3.1.1–3.1.2). The Biot model is the most widely used in the literature for the modeling of poro-elastic effects in the arterial wall. Here, we propose to investigate the recent model proposed by the M3DISIM project-team in 58, which allows for nonlinear constitutive behaviors and viscous effects, both in the fluid and the solid. Among the questions which will be addressed, some of them in collaboration with M3DISIM, we mention the justification of the model (or its linearized version) by means of homogenization techniques and its well-posedness.

Fluid–particle interaction. Mathematical analysis studies on the Navier-Stokes-Vlasov system for fluid-particle interaction in aerosols can be found in 48, 50. We propose to extend these studies to more realistic models which take into account, for instance, changes in the volume of the particles due to humidity.

3.3.2 Numerical methods for multi-physics problems

In this section we describe the main research directions that we propose to explore as regards the numerical approximation of multi-physics problems.

Fluid-structure interaction. The spatial discretization of fluid-structure interaction (FSI) problems generally depends on the amount of solid displacement within the fluid. Problems featuring moderate interface displacements can be successfully simulated using (moving) fitted meshes with an arbitrary Lagrangian-Eulerian (ALE) description of the fluid. This facilitates, in particular, the accurate discretization of the interface conditions. Nevertheless, for problems involving large structural deflections, with solids that might come into contact or that might break up, the ALE formalism becomes cumbersome. A preferred approach in this case is to combine an Eulerian formalism in the fluid with an unfitted mesh discretization, in which the fluid-structure interface deforms independently of a background fluid mesh. In general, traditional unfitted mesh approaches (such as the immersed boundary and the fictitious domain methods 76, 47, 65, 45) are known to be inaccurate in space. These difficulties have been recently circumvented by a Nitsche-based cut-FEM methodolgy (see 42, 52). The superior accuracy properties of cut-FEM approaches comes at a price: these methods demand a much more involved computer implementation and require a specific evaluation of the interface intersections.

As regards the time discretization, significant advances have been achieved over the last decade in the development and the analysis of time-splitting schemes that avoid strong coupling (fully implicit treatment of the interface coupling), without compromising stability and accuracy. In the vast majority these studies, the spatial discretization is based on body fitted fluid meshes and the problem of accuracy remains practically open for the coupling with thick-walled structures (see, e.g., 63). Within the unfitted mesh framework, splitting schemes which avoid strong coupling are much more rare in the literature.

Computational efficiency is a major bottleneck in the numerical simulation of fluid-structure interaction problems with unfitted meshes. The proposed research activity is aimed at addressing these issues. Another fundamental problem that we propose to face is the case of topology changes in the fluid, due to contact or fracture of immersed solids. This challenging problem (fluid-structure-contact-fracture interaction) has major role in many applications (e.g., heart valves repair or replacement, break-up of drug-loaded micro-capsules) but most of the available studies are still merely illustrative. Indeed, besides the numerical issues discussed above, the stability and the accuracy properties of the numerical approximations in such a singular setting are not known.

Fluid–particle interaction and gas diffusion.

Aerosols can be described through mesoscopic equations of kinetic type, which provide a trade-off between model complexity and accuracy. The strongly coupled fluid-particle system involves the incompressible Navier-Stokes equations and the Vlasov equation. The proposed research activity is aimed at investigating the theoretical stability of time-splitting schemes for this system. We also propose to extend these studies to more complex models that take into account the radius growth of the particles due to humidity, and for which stable, accurate and mass conservative schemes have to be developed.

As regards gas diffusion, the mathematical models are generally highly non-linear (see, e.g., 73, 75, 49). Numerical difficulties arise from these strong non linearities and we propose to develop numerical schemes able to deal with the stiff geometrical terms and that guarantee mass conservation. Moreover, numerical diffusion must be limited in order to correctly capture the time scales and the cross-diffusion effects.

3.3.3 Statistical learning and mathematical modeling interactions

Machine learning and in general statistical learning methods (currently intensively developed and used, see 43) build a relationship between the system observations and the predictions of the QoI based on the a posteriori knowledge of a large amount of data. When dealing with biomedical applications, the available observations are signals (think for instance to images or electro-cardiograms, pressure and Doppler measurements). These data are high dimensional and the number of available individuals to set up precise classification/regression tools could be prohibitively large. To overcome this major problem and still try to exploit the advantages of statistical learning approaches, we try to add, to the a posteriori knowledge of the available data an a priori knowledge, based on the mathematical modeling of the system. A large number of numerical simulations is performed in order to explore a set of meaningful scenarios, potentially missing in the dataset. This in silico database of virtual experiments is added to the real dataset: the number of individuals is increased and, moreover, this larger dataset can be used to compute semi-empirical functions to reduce the dimension of the observed signals.

Several investigations have to be carried out to systematically set up this framework. First, often there is not a single mathematical model describing a physiological phenomenon, but hierarchies of model of different complexity. Every model is characterized by a model error. How can this be accounted for? Moreover, several statistical estimators can be set up and eventually combined together in order to improve the estimations (see 81). Other issues have an actual impact and has to be investigated: what is the optimal number of in silico experiments to be added? What are the most relevant scenarios to be simulated in relation to the statistical learning approach considered in order to obtain reliable results? In order to answer to these questions, discussions and collaborations with statistics and machine learning groups have to be developed.

3.3.4 Tensor approximation and HPC

Tensor methods have a recent significant development because of their pertinence in providing a compact representation of large, high-dimensional data. Their applications range from applied mathematics and numerical analysis to machine learning and computational physics. Several tensor decompositions and methods are currently available (see 67). Contrary to matrices, for tensors of order higher or equal to three, there does not exist, in general, a best low rank approximation, the problem being ill posed (see 79). Two main points will be addressed: (i) The tensor construction and the multi-linear algebra operations involved when solving high-dimensional problems are still sequential in most of the cases. The objective is to design efficient parallel methods for tensor construction and computations; (ii) When solving high-dimensional problems, the tensor is not assigned; instead, it is specified through a set of equations and tensor data. Our goal is to devise numerical methods able to (dynamically) adapt the rank and the discretization (possibly even the tensor format) to respect the chosen error criterion. This could, in turn, improve the efficiency and reduce the computational burden.

These sought improvements could make the definition of parsimonious discretizations for kinetic theory and uncertainty quantification problems (see Section 3.2.1) more efficient and suitable for a HPC paradigm. This work will be carried out in collaboration with Olga Mula (Université Paris-Dauphine) and the ALPINES and MATHERIALS project-teams.

6.1.1 FELiScE

• Name: Finite Elements for Life SCiences and Engineering problems
• Keywords: Finite element modelling, Cardiac Electrophysiology, Cardiovascular and respiratory systems
• Functional Description: FELiScE is a finite element code which the M3DISIM and REO project-teams initially jointly develop in order to build up on their respective experiences concerning finite element simulations. One specific objective of this code is to provide in a unified software environment all the state-of-the-art tools needed to perform simulations of the complex respiratory and cardiovascular models considered in the two teams – namely involving fluid and solid mechanics, electrophysiology, and the various associated coupling phenomena. FELISCE is written in C++, and may be later released as an opensource library. FELiScE was registered in July 2014 at the Agence pour la Protection des Programmes under the Inter Deposit Digital Number IDDN.FR.001.350015.000.S.P.2014.000.10000.
• URL: https://team.inria.fr/commedia/software/felisce/
• Authors: Jean-Frédéric Gerbeau, Miguel Ángel Fernández, Dominique Chapelle, Marina Vidrascu, Philippe Moireau
• Contact: Miguel Ángel Fernández
• Participants: Matteo Aletti, Daniele Carlo Corti, Dominique Chapelle, Miguel Ángel Fernández, Benoit Fabreges, Axel Fourmont, Jean-Frédéric Gerbeau, Fannie Gerosa, Sébastien Gilles, Mikel Landajuela Larma, Damiano Lombardi, Vicente Mataix Ferrandiz, Philippe Moireau, Irène Vignon-Clementel, Marina Vidrascu

6.1.2 FELiScE-NS

• Keywords: Incompressible flows, Thin-walled solids
• Functional Description: FELiScE-NS is a set finite elements solvers for incompressible fluids (fractional-step schemes) and non-linear thin-walled structures (3D shells, and 2D curved beams) developed in the framework of the FELiScE library. FELiSCe-NS was registered in 2018 at the Agence pour la Protection des Programmes Inter Deposit Digital Number IDDN.FR.001.270015.000.S.A.2018.000.31200.
• Authors: Benoit Fabreges, Axel Fourmont, Miguel Ángel Fernández, Jean-Frédéric Gerbeau, Marina Vidrascu
• Contact: Miguel Ángel Fernández
• Participants: Benoit Fabreges, Miguel Ángel Fernández, Axel Fourmont, Jean-Frédéric Gerbeau, Marina Vidrascu

6.1.3 DCIMaL

• Keyword: Cardiac Electrophysiology
• Functional Description: DCIMaL is a Python and C++ software for safety pharmacology studies and particularly field potentials signals measured with micro-electrode array (MEA). The software includes a solver for field potential simulations and a dictionary of entries corresponding to features which can be extracted from real or simulated potential signals. It also includes an algorithm for drug classification (channel blockade or torsadogenic risk) and a tool for estimating ion channel activity (based on the CMAES library). DCIMaL was registered in 2018 at the Agence pour la Protection des Programmes Inter Deposit Digital Number IDDN.FR.001.270003.000.S.P.2018.000.31230
• Authors: Jean-Frédéric Gerbeau, Damiano Lombardi, Fabien Raphel
• Contact: Damiano Lombardi
• Participants: Fabien Raphel, Jean-Frédéric Gerbeau, Damiano Lombardi

Notocord Systems

Participants: Damiano Lombardi, Fabien Raphel.

This work is devoted to the investigation on new approaches and efficient algorithms in the context of safety pharmacology and the analysis of biological signals.

Casis

Participants: Mocia Agbalessi, Miguel Ángel Fernández Varela, Damiano Lombardi.

This work is devoted to the combination of 4D-MRI data and fluid-structure interaction models of blood flow to asses indicators of aneurysm rupture.

9.1.1 Inria associate team not involved in an IIL

IMFIBIO: Innovative Methods for Forward and Inverse problems in BIO-medical applications

Participants: Muriel Boulakia, Daniele Corti, Miguel Ángel Fernández Varela, Fannie Gerosa, Céline Grandmont.

• Duration: 2020-2022
• Coordinator: Muriel Boulakia
• Partner: Department of Mathematics, University College London (UK)
• Summary: The purpose of the IMFIBIO Associate Team is to exploit the complementary expertise of both partners in mathematical analysis, numerical analysis, scientific computing and data assimilation in order to develop innovative methods for the study of forward and inverse problems in the context of bio-medical applications.
• Web site: https://team.inria.fr/imfibio/

9.1.2 Visits to international teams

9.2.1 FP7 & H2020 Projects

INSPIRE: INnovation in Safety Pharmacology for Integrated cardiovascular safety assessment to REduce adverse events and late stage drug attrition

Participants: Muriel Boulakia, Sara Costa Faya, Miguel Ángel Fernández Varela, Céline Grandmont, Haibo Liu, Damiano Lombardi.

• Funding: Horizon 2020 - MSCA-ITN
• Duration: 2020-2023
• Coordinator: University of Antwerp
• Local coordinator: Damiano Lombardi
• Partners: see the link
• Summary: INSPIRE is an European Training Network (ETN) projet funding 15 Early Stage Researchers (ESRs) aimed to exploit innovative techniques for better assessment and prediction of cardiovascular safety liabilities.
• Web site: https://www.uantwerpen.be/en/projects/inspire-safety-pharmacology/

9.3.1 ANR

ADAPT: Adaptive Dynamical Approximations by Parallel Tensor methods

Participants: Maria Fuente-Ruiz, Damiano Lombardi, Olga Mula.

• Funding: ANR JCJC
• Duration: 2018-2022
• Coordinator: Damiano Lombardi
• Summary: The main goal of the ANR is to investigate the numerical approximation of the solution of high-dimensional problems. In particular, the applications that motivate this study are the Uncertainty Quantification and the Kinetic theory. The main objective is to construct in an adaptive way parsimonious discretisations starting from arbitrarily chosen separated discretisations.
• Web site: https://project.inria.fr/adapt/

SIMR: Simulation and Imaging for Mitral Regurgitation

Participants: Daniele Carlo Corti, Miguel Ángel Fernández Varela, Fannie Gerosa, Céline Grandmont, Marina Vidrascu.

• Funding: ANR PRC
• Duration: 2020-2023
• Coordinator: Miguel Ángel Fernández Varela
• Partners: CREATIS, HCL, LGEF, M3DISIM, TIMC
• Summary: The SIMR project aims at evaluating the physical consequences of mitral repair using efficient numerical simulations, advanced imaging techniques and an innovative measurement tools in a clinical study.
• Web site: https://project.inria.fr/simr/

10.1.1 Scientific events: organisation

• Damiano Lombardi
  • Co-organizer of the CEMRACS 2021 summer school.
  • Co-organizer of Inria-LJLL meeting in scientific computing
• Olga Mula
  • Co-organizer of the CEMRACS 2021 summer school.
  • Mini-symposium co-organizer (with B. Desprès, M. Campos- Pinto and O. Laffite.) Numerical Aspects of Transport, Boltzmann and Kinetic Equations,WCCM-ECCOMAS 2020 conference, Paris

10.1.2 Journal

10.1.3 Scientific expertise

• Olga Mula
  • Member of "Facing the virus", a scientific initiative on Covid-19 launched by researchers from PSL Univ.
  • Member of the "Liaison Committee of SIGMA", the activity group on Signal-Image-Geometry-Modelling-Approximation.

10.1.4 Research administration

• Miguel Ángel Fernández Varela
  • Head of Science, Inria Paris
  • Member of the Inria Evaluation Committee
• Céline Grandmont
  • Member of the Inria Evaluation Committee
  • Member of the Inria Parity Committee
  • Member of the scientific board of the EDMH, Paris Saclay
• Damiano Lombardi
  • Member of LJLL Conseil du Laboratoire

10.1.5 Conferences

• Muriel Boulakia
  • Seminar, Workshop on Collective behavior of particles in fluids , December 2020, IHP Paris
  • Seminar MEDISIM-POEMS-DEFI, June 2020, Inria Saclay
• Daniele Corti
  • Contributed talk, WCCM-ECCOMAS 2020 conference, Paris
• Miguel Ángel Fernández Varela
  • Contributed talk, WCCM-ECCOMAS 2020 conference, Paris
• Fannie Gerosa
  • Contributed talk, WCCM-ECCOMAS 2020 conference, Paris
• Céline Grandmont
  • Invited Speaker, Mathematics of Complex Systems in Biology and Medicin conference, Feb 2020, Cirm Marseille
• Damiano Lombardi
  • Seminar, Eindhoven Univ of Technology, September 2020
• Olga Mula
  • Seminar Rencontres INRIA-LJLL, December 2020
  • Seminar of the Jean-Leray Institute, November 2020
  • Seminar of the MaNu activity group, June 2020
  • Seminar MEDISIM-POEMS-DEFI (Inria Saclay), June 2020
  • Working Group at Dauphine on COVID-19, June 2020
  • Seminar ANEDP at Univ de Lille, March 2020
  • Journée des 50 ans du CEREMADE (Univ Paris-Dauphine), January 2020
• Fabien Raphel
  • Math-bio GDT Marseille
• Marina Vidrascu
  • Contributed talk at WCCM-ECCOMAS 2020 conference, Paris

10.2.1 Teaching

• Licence:
  • Muriel Boulakia
    • Projects on differential equations, 12h, L3, Polytech Sorbonne, Sorbonne Univ
    • Nonlinear systems and optimization, 36h, L3, Polytech Sorbonne, Sorbonne Univ
    • Mathematics for scientific studies, 50h, L1, Sorbonne Univ
  • Felipe Galarce Marin
    • TD Numerical methods for differential equations, 24h, L3, Sorbonne Univ
  • Fannie Gerosa
    • TD Numerical methods for differential equations, 26h, L3, Sorbonne Univ
  • Damiano Lombardi
    • PGD methods , 3 h, ENPC.
    • TP, Numerical Analysis, L3, 24h, Sorbonne Univ
  • Fabien Raphel
    • TP Numerical Analysis in Python 12h, L3, Sorbonne Univ
• Master:
  • Muriel Boulakia
    • Tutorials on Basis of Functional Analysis (distance learning), 30h, M1, Sorbonne Univ
    • Preparatory course for teaching admission examination “Agrégation”, 60h, M2, Sorbonne Univ
  • Miguel Ángel Fernández Varela
    • Modeling and numerical methods for hemodynamics, 30h, M2, Sorbonne Univ
  • Damiano Lombardi
    • Mini-course Modeling the electrophysiology of heart, 4.5h, M2, Ecole des Mines Paristech
    • TD, Numerical Methods, M1, 15 h, Sorbonne Univ

10.2.2 Supervision

• PhD in progress: Mocia Agbalessi, Modeling and patient specific fluid-structure interaction simulations of aortic pathological configurations. Since April, 2019, Supervisors: M.A. Fernández Varela & D. Lombardi
• PhD in progress: Marguerite Champion, Modeling and numerical simulation of implantable aortic blood pumps. Since November 2020. Supervisors: M.A. Fernández Varela, C. Grandmont & M. Vidrascu
• PhD in progress: Daniele Corti, Modeling and numerical simulation of the mitral apparatus. Since October 2020. Supervisors: M.A. Fernández Varela, F. Alauzet and G. Delay & M. Vidrascu
• PhD in progress: Sara Costa Faya, An in silico approach to monitor and predict haemodynamics during safety pharmacology studies., since September 2020. M.A. Fernández Varela, C. Grandmont & D.Lombardi
• PhD in progress: Maria Fuente Ruiz , Adaptive tensor methods for scientific computing. Since March 2020. Supervisors: D. Lombardi & V. Ehrlacher
• PhD in progress: Felipe Galarce, Enhancing hemodynamics measurements with mathematical modeling, since December 2017. Supervisors: J.-F. Gerbeau, D. Lombardi & O. Mula
• PhD in progress: Fannie Gerosa, Immersed boundary methods for fluid-structure interaction with topological changes, since January 2018. Supervisor: M.A. Fernández Varela
• PhD in progress: Fabien Lespagnol, A new computational approach for fluid-structure interaction of slender bodies immersed in three-dimen- sional flow. Since September 2020. Supervisors: M. Boulakia, M.A. Fernández Varela, C. Grandmont & Paolo Zunino (MOX, Politechnico de Milano)
• PhD in progress: Haibo Liu, Data assimilation for high-throughput screening in safety pharmacology. Since September 2020. Supervisors: D. Lombardi & M. Boulakia
• PhD in progress: Fabien Raphel, Mathematical modeling and learning of biomedical signals for safety pharmacology. Since April 2019. Supervisors: J.-F. Gerbeau & D. Lombardi

10.2.3 Juries

• Muriel Boulakia
  • PHD committee: Nicolas Molina, Univ Paris-Dauphine (reviewer), Imene Djebour, IECL Nancy
• Miguel Ángel Fernández Varela
  • Hiring committees: Inria CRCN Paris and CRCN National
  • PHD committee: Daniel Grinberg MD, INSA Lyon (reviewer)
• Céline Grandmont
  • Hiring committee Assistant Professor Univ Orléans
  • Hiring committees: Inria CRCN Lille, Inria DR2
  • PHD committee: Nicola Molinas, Univ Dauphine
  • Blaise Pascal SMAI Prize
• Damiano Lombardi
  • PHD committee: Roman Weinhadl, Otto von Guericke Univ and Max Planck Magdebourg (reviewer) and Sebastien Riffaud, Univ. Bordeaux
• Olga Mula
  • Hiring committee Assistant Professor UTC Compiègne, Univ Stasbourg
  • PHD committee: Antonio Galia, Univ Paris-Saclay

10.3.1 Interventions

• Céline Grandmont
  • Conference Filles et Maths, Villetaneuse, February 2020

International journals

• 10 article AurélienA. Alfonsi, RafaëlR. Coyaud, VirginieV. Ehrlacher and DamianoD. Lombardi. Approximation of Optimal Transport problems with marginal moments constraints Mathematics of Computation 2020
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• 11 articleAthmaneA. Bakhta, ThomasT. Boiveau, YvonY. Maday and OlgaO. Mula. Epidemiological Forecasting with Model Reduction of Compartmental Models. Application to the COVID-19 PandemicBiology101December 2020, 22
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• 12 articleLaurentL. Boudin, CélineC. Grandmont, BéréniceB. Grec, SébastienS. Martin, AminaA. Mecherbet and FrédériqueF. Noël. Fluid-kinetic modelling for respiratory aerosols with variable size and temperatureESAIM: Proceedings and Surveys672020, 100-119
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• 13 article MurielM. Boulakia, ErikE. Burman, Miguel AngelM. Fernández and ColetteC. Voisembert. Data assimilation finite element method for the linearized Navier-Stokes equations in the low Reynolds regime Inverse Problems 36 8 May 2020
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• 14 article MurielM. Boulakia, MayaM. De Buhan and EricaE. Schwindt. Numerical reconstruction based on Carleman estimates of a source term in a reaction-diffusion equation. ESAIM: Control, Optimisation and Calculus of Variations 2021
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• 15 article AlbertA. Cohen, WolfgangW. Dahmen, RonR. DeVore, JalalJ. Fadili, OlgaO. Mula and JamesJ. Nichols. Optimal reduced model algorithms for data-based state estimation SIAM Journal on Numerical Analysis June 2020
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• 16 article WolfgangW. Dahmen, FelixF. Gruber and OlgaO. Mula. An Adaptive Nested Source Term Iteration for Radiative Transfer Equations Mathematics of Computation January 2020
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• 17 article VirginieV. Ehrlacher, LauraL. Grigori, DamianoD. Lombardi and HaoH. Song. Adaptive hierarchical subtensor partitioning for tensor compression SIAM Journal on Scientific Computing 2021
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• 18 article VirginieV. Ehrlacher, DamianoD. Lombardi, OlgaO. Mula and François-XavierF.-X. Vialard. Nonlinear model reduction on metric spaces. Application to one-dimensional conservative PDEs in Wasserstein spaces ESAIM: Mathematical Modelling and Numerical Analysis 2020
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• 19 article Miguel AngelM. Fernández and FannieF. Gerosa. An unfitted mesh semi-implicit coupling scheme for fluid-structure interaction with immersed solids International Journal for Numerical Methods in Engineering May 2020
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• 20 articleMiguel AngelM. Fernández and MikelM. Landajuela. Splitting schemes and unfitted mesh methods for the coupling of an incompressible fluid with a thin-walled structureIMA Journal of Numerical Analysis402April 2020, 1407–1453
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• 21 article FelipeF. Galarce, Jean-FrédéricJ.-F. Gerbeau, DamianoD. Lombardi and OlgaO. Mula. Fast reconstruction of 3D blood flows from Doppler ultrasound images and reduced models Computer Methods in Applied Mechanics and Engineering March 2021
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• 22 article FelipeF. Galarce, DamianoD. Lombardi and OlgaO. Mula. Reconstructing Haemodynamics Quantities of Interest from Doppler Ultrasound Imaging International Journal for Numerical Methods in Biomedical Engineering 2020
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• 23 article DamianoD. Lombardi, YvonY. Maday and LydieL. Uro. Fast semi-automatic segmentation based on reduced basis Comptes Rendus Mathématique 2020
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• 24 article YvonY. Maday and OlgaO. Mula. An Adaptive Parareal Algorithm Journal of Computational and Applied Mathematics May 2020
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• 25 articleJulienJ. Ratelade, NicholasN. Klug, DamianoD. Lombardi, Monara Kaelle Servulo CruzM. Angelim, FabriceF. Dabertrand, ValérieV. Domenga-Denier, RustamR. Al-Shahi Salman, ColinC. Smith, Jean-FrédéricJ.-F. Gerbeau, MarkM. Nelson and AnneA. Joutel. Reducing Hypermuscularization of the Transitional Segment between Arterioles and Capillaries Protects Against Spontaneous Intracerebral HemorrhageCirculation14125March 2020, 2078-2094
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• 26 article DavidD. Sampedro-Puente, FabienF. Raphel, JesusJ. Fernandez-Bes, PabloP. Laguna, DamianoD. Lombardi and EstherE. Pueyo. Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and under beta-Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction IEEE Journal of Biomedical and Health Informatics April 2020
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• 27 articleAlexandreA. This, LudovicL. Boilevin-Kayl, Miguel AngelM. Fernández and Jean-FrédéricJ.-F. Gerbeau. Augmented Resistive Immersed Surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phasesInternational Journal for Numerical Methods in Biomedical Engineering363February 2020, e3223
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• 28 articleAlexandreA. This, Hernan GH. Morales, OdileO. Bonnefous, Miguel AngelM. Fernández and Jean-FrédéricJ.-F. Gerbeau. A pipeline for image based intracardiac CFD modeling and application to the evaluation of the PISA methodComputer Methods in Applied Mechanics and Engineering358January 2020, 112627
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Doctoral dissertations and habilitation theses

• 29 thesis DamianoD. Lombardi. Contributions to scientific computing for data-simulation interaction in biomedical applications Sorbonne Université July 2020
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Reports & preprints

• 30 misc JoubineJ. Aghili and OlgaO. Mula. Depth-Adaptive Neural Networks from the Optimal Control viewpoint July 2020
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• 31 misc MicheleM. Annese, Miguel AngelM. Fernández and LuciaL. Gastaldi. Splitting schemes for a Lagrange multiplier formulation of FSI with immersed thin-walled structure: stability and convergence analysis July 2020
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• 32 report JamalJ. Atif, BertrandB. Cabot, OlivierO. Cappé, OlgaO. Mula and RafaelR. Pinot. Initiative face au virus. Regards croisés sur l'épidemie de Covid-19 apportés par les données sanitaires et de géolocalisation (mars à octobre 2020) Université PSL; Inria; CNRS December 2020
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• 33 report JamalJ. Atif, OlivierO. Cappé, AkinA. Kazakçi, YannickY. Léo, LaurentL. Massoulié and OlgaO. Mula. Initiative face au virus Observations sur la mobilité pendant l'épidémie de Covid-19 Université PSL May 2020
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• 34 report LuceL. Brotcorne, AnneA. Canteaut, Aline CarneiroA. Viana, CélineC. Grandmont, BenjaminB. Guedj, StéphaneS. Huot, ValérieV. Issarny, GuillaumeG. Pallez, ValérieV. Perrier, VivienV. Quema, Jean-BaptisteJ.-B. Pomet, XavierX. Rival, SylvainS. Salvati and EmmanuelE. Thomé. Indicateurs de suivi de l'activité scientifique de l'Inria Inria December 2020
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• 35 misc ErikE. Burman, RebeccaR. Durst, Miguel AngelM. Fernández and JohnnyJ. Gunzmán. Fully discrete loosely coupled Robin-Robin scheme for incompressible fluid-structure interaction: stability and error analysis July 2020
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• 36 misc ErikE. Burman, Miguel AngelM. Fernández and FannieF. Gerosa. Error analysis of an unfitted mesh semi-implicit coupling scheme for fluid-structure interaction December 2020
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• 37 report AnneA. Canteaut, Miguel AngelM. Fernández, LucL. Maranget, SophieS. Perin, MarioM. Ricchiuto, ManuelM. Serrano and EmmanuelE. Thomé. Évaluation des Logiciels Inria January 2021
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• 38 report AnneA. Canteaut, Miguel AngelM. Fernández, LucL. Maranget, SophieS. Perin, MarioM. Ricchiuto, ManuelM. Serrano and EmmanuelE. Thomé. Software Evaluation Inria January 2021
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• 39 misc AlbertA. Cohen, WolfgangW. Dahmen, OlgaO. Mula and JamesJ. Nichols. Nonlinear reduced models for state and parameter estimation September 2020
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• 40 misc VirginieV. Ehrlacher, MariaM. Fuente-Ruiz and DamianoD. Lombardi. CP-TT: using TT-SVD to greedily construct a Canonical Polyadic tensor approximation November 2020
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• 41 misc CélineC. Grandmont and FabienF. Vergnet. Existence and uniqueness for a quasi-static interaction problem between a viscous fluid and an active structure February 2020
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