Damiano Lombardi

Inverse problems in 1D hemodynamics on systemic networks: A sequential approach

In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented.

SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS

A tumor growth model based on a parametric system of partial differential equations is considered. The system corresponds to a phenomenological description of a multi-species population evolution. A velocity field taking into account the volume increase due to cellular division is introduced and the mechanical closure is provided by a Darcy-type law. The complexity of the biological phenomenon is taken into account through a set of parameters included in the model that need to be calibrated. To this end, a system identification method based on a low-dimensional representation of the solution space is introduced. We solve several idealized identification cases corresponding to typical situations where the information is scarce in time and in terms of observable fields. Finally, applications to actual clinical data are presented.

Approximated Lax pairs for the reduced order integration of nonlinear evolution equations

A reduced-order model algorithm, called ALP, is proposed to solve nonlinear evolution partial differential equations. It is based on approximations of generalized Lax pairs. Contrary to other reduced-order methods, like Proper Orthogonal Decomposition, the basis on which the solution is searched for evolves in time according to a dynamics specific to the problem. It is therefore well-suited to solving problems with progressive front or wave propagation. Another difference with other reduced-order methods is that it is not based on an off-line/on-line strategy. Numerical examples are shown for the linear advection, KdV and FKPP equations, in one and two dimensions.

A lagrangian scheme for the solution of the optimal mass transfer problem

A lagrangian method to numerically solve the L^2 optimal mass transfer problem is presented. The initial and final density distributions are approximated by finite mass particles having a gaussian kernel. Mass conservation and the Hamilton-Jacobi equation for the potential are identically satisfied by constant mass transport along straight lines. The scheme is described in the context of existing methods to solve the problem and a set of numerical examples including applications to medical imagery are presented.

A dynamical adaptive tensor method for the Vlasov–Poisson system

Abstract A numerical method is proposed to solve the full-Eulerian time-dependent Vlasov–Poisson system. The algorithm relies on the construction of a tensor decomposition of the solution whose rank is adapted at each time step. This decomposition is obtained through the use of an efficient modified Progressive Generalized Decomposition (PGD) method, whose convergence is proved. We suggest in addition a symplectic time-discretization splitting scheme that preserves the Hamiltonian properties of the system. This scheme is naturally obtained by considering the tensor structure of the approximation. The proposed approach is illustrated through time-dependent 1D–1D, 2D–2D and 3D–3D numerical examples.

Reduced Order Models at Work in Aeronautics and Medicine

We review a few applications of reduced-order modeling in aeronautics and medicine. The common idea is to determine an empirical approximation space for a model described by partial differential equations. The empirical approximation space is usually spanned by a small number of global modes. In case of time-periodic or mainly diffusive phenomena it is shown that this approach can lead to accurate fast simulations of complex problems. In other cases, models based on definition of transport modes significantly improve the accuracy of the reduced model.

Some Models for the Prediction of Tumor Growth: General Framework and Applications to Metastases in the Lung

This chapter presents an example of an application of a mathematical model: the goal is here to help clinicians evaluate the aggressiveness of some metastases to the lung. For this matter, an adequate spatial model is described and two algorithms (one using a reduced model approach and the other one a sensitivity technique) are shown. They allow us to find reasonable values of the parameters of this model for a given patient with a sequence of medical images. The quality of the prognosis obtained through the calibrated model is then illustrated with several clinical cases.