In this paper, we present a generic approach of a dynamical data-driven model order reduction technique for three-dimensional fluid-structure interaction problems. A low-order continuous linear differential system is identified from snapshot solutions of a high-fidelity solver. The reduced order model (ROM) uses different ingredients like proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and Tikhonov-based robust identification techniques.
View Article and Find Full Text PDFAn innovative data-driven model-order reduction technique is proposed to model dilute micrometric or nanometric suspensions of microcapsules, i.e., microdrops protected in a thin hyperelastic membrane, which are used in Healthcare as innovative drug vehicles.
View Article and Find Full Text PDFMelanoblasts are a particular type of cell that displays extensive cellular proliferation during development to contribute to the skin. There are only a few melanoblast founders, initially located just dorsal to the neural tube, and they sequentially colonize the dermis, epidermis, and hair follicles. In each compartment, melanoblasts are exposed to a wide variety of developmental cues that regulate their expansion.
View Article and Find Full Text PDFWe aim to evaluate environmental and genetic effects on the expansion/proliferation of committed single cells during embryonic development, using melanoblasts as a paradigm to model this phenomenon. Melanoblasts are a specific type of cell that display extensive cellular proliferation during development. However, the events controlling melanoblast expansion are still poorly understood due to insufficient knowledge concerning their number and distribution in the various skin compartments.
View Article and Find Full Text PDFIn this paper, we are looking for mathematical modeling of mouse embryonic melanoblast proliferation dynamics, taking into account, the expression level of β-catenin. This protein plays an important role into the whole signal pathway process. Different assumptions on some unobservable features lead to different candidate models.
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