Hamidreza Dehghani: Multiscale Poro-Hyperelasticity using ANNs

Machine Learning Seminar presentation

Topic: Multiscale Poro-Hyperelasticity using ANNs

Speaker: Hamidreza Dehghani, FSTM, University of Luxembourg

Time: Wednesday, 2021.04.14, 10:00 CET

How to join: Please contact Jakub Lengiewicz


Poroelastic media are composed of deformable porous solid with viscous fluid percolating its pores. When the solid compartment undergoes large deformation, a hyperelastic model is required (Poro-Hyperelasticity) which is usually carried out via phenomenological homogenized models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localization procedures, we propose an incremental numerical approach for the analysis of poroelastic media under finite deformation considering infinitesimal strain in each time increment. The Darcy’s experiment is reconstructed numerically and the mechanical response of brain tissue under uniaxial cyclic test is simulated and studied.

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