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DTSTART;TZID=Europe/Stockholm:20230627T123000
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UID:submissions.pasc-conference.org_PASC23_sess173_msa215@linklings.com
SUMMARY:Deep Learning-Based Reduced Order Modeling for Microcirculation in
  Vascular Networks
DESCRIPTION:Minisymposium\n\nNicola Rares Franco, Piermario Vitullo, Andre
 a Manzoni, and Paolo Zunino (Politecnico di Milano)\n\nPhysics-based model
 s describing biological phenomena in mathematical terms usually rely on nu
 merical simulations to derive physically interpretable biomarkers, ultimat
 ely supporting decisions in clinical treatments. Calibrating model paramet
 ers in this context requires a suitable combination of uncertainty quantif
 ication tools and efficient numerical solvers capable of repeated simulati
 ons in very rapid times. In this talk we focus on the use of recently esta
 blished Deep Learning-based reduced order models (DL-ROMs) for the efficie
 nt approximation of the parameter-to-solution map of parametrized partial 
 differential equations in the case of perfusion models, ultimately aiming 
 at the personalized treatment planning of radiotherapy for oncological pat
 ients. In this scenario, we rely on Tumor Control Probability (TCP) models
 , that exploit the relationship between oxygen partial pressure and resist
 ance to radiation to estimate the probability of tumor eradication, aiming
  at the approximation of maps from a set of model parameters (such as, e.g
 ., the permeability of the blood vessels and the topology of the vascular 
 network) to the oxygen partial pressure. To properly account for the impac
 t of the topology of the microvascular network on the oxygen distribution 
 in the tissue, we rely on DL-ROMs and newly introduced mesh-informed neura
 l network architectures, offering the possibility to handle geometrical in
 formation efficiently.\n\nDomain: Life Sciences\n\nSession Chair: Georgios
  Kissas (University of Pennsylvania)
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