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Applied Math and Comp Sci Colloquium

Friday, December 14, 2018 - 2:00pm

Alireza Yazdani

Brown University


University of Pennsylvania

Room 534, 3401 Walnut Street, Wing A

Inspired by the recent developments in physics-informed deep learning  framework, we propose a novel Navier-Stokes informed neural networks that encodes the governing equations of fluid motions i.e., mass, momentum and transport equations and infer hidden quantities of interest such as velocity and pressure fields merely from spatio-temporal visualizations of a passive scaler (e.g., dye or smoke) transported in arbitrarily complex domains. Our approach towards solving the aforementioned data assimilation problem is unique as we design an algorithm that is agnostic to the geometry or the initial and boundary conditions. Furthermore, I will discuss the recent developments in addressing complex biological systems at both micro- and macroscopic scales and show the performance of our modeling strategies in addressing thrombus formation in aortic dissections — a life-threatening event that is initiated by the damage in arterial wall propagating within the media layer and connecting with the true lumen to form a so-called false lumen within the aortic wall — by using in-vivo and in-vitro data collected for murine dissections. I will present a data-driven multiscale framework to elucidate hemodynamic and biochemical conditions under which a thrombus forms.