Choix, piloté par les données, de la paramétrisation de modèles réduits pour des applications cardiovasculaires et respiratoires multi-échelles

DATA DRIVEN CHOICE OF REDUCED MODEL PARAMETRIZATION FOR MULTI SCALE CARDIOVASCULAR AND RESPIRATORY APPLICATIONS

Multiscale modeling is commonly used to assess in more detail the flow in a 3D region of interest, while the rest of the blood circulation or respiratory system is taken into account with reduced models (0D or 1D models). The latter constitute boundary conditions for the 3D part, and drive most of its dynamics. They are thus crucial for the model. Yet, their parameters are a priori unknown: they need to be identified in order to reflect blood or air flow measurements.
Depending on the considered application, available measurements vary. Depending on the acquisition, full time-varying curves or only mean values are trusted. In this talk, we will present several challenges in this topic: 1) measurements are not necessarily taken at boundaries of the 3D domain, 2) they are often too few to identify all parameters, and thus need to be complemented by modeling assumptions or literature data, 3) they are usually not taken simultaneously and are thus not synchronized in time, 4) computational complexity for parameter identification.
Strategies should be devised according to the available measurements: their implementation and computational complexity need to be coherent with the amount of information. Thus parameter identification can be done on purely 0D models, on loosely coupled 3D-0D models, or on strongly-coupled 3D-0D models (in the sense that each parameter identification simulation includes the 3D part). We will show different real application examples