Τύπος: 
Σεμινάριο 
Ημ/νία Έναρξης: 
8/6/2022 
Ημ/νία Λήξης: 
8/6/2022 
Ώρα Έναρξης: 
11.00 
Ώρα Λήξης: 
12.00 
Ομιλητής: 
Nikolaos Bouklas, Assistant Professor, Cornell University 
Τίτλος: 
Utilizing data and physical constraints in machine learning enabled computational solid mechanics and multiphysics 
Περιγραφή: 
Abstract
Machine learning techniques are gearing up to play a significant role in the field of computational solid mechanics and multiphysics, enabling the integration of experimental data and physical constraints towards datadriven constitutive laws, acceleration of computational techniques for multiscale modeling, and new paradigms for the solution of forward and inverse problems, to name a few. This talk will cover recent advancements in the aforementioned areas: I) A physicsinformed datadriven constitutive modeling approach for isotropic and anisotropic hyperelastic materials is developed using tensor representation theorems. The trained laGPR surrogates are able to respect physical principles such as material frame indifference, material symmetry, and the local balance of angular momentum. Overall, the presented approach is tested on synthetic data from isotropic and anisotropic constitutive laws and shows surprising accuracy even far beyond the limits of the training domain, indicating that the resulting surrogates can efficiently generalize as they incorporate knowledge about the underlying physics. II) Finally, a datadriven framework is presented based on the usual offlineonline paradigm for solving PDEs, focusing on complex microstructures in the context of both forward and inverse problems. The framework is developed based on conditional and patchbased generative adversarial networks (GAN), typically used in image/video analysis. Here we will focus on forward and inverse problems, as well as an extension to time dependent problems in the context of poroelasticity.

Χώρος: 
Αίθουσα Συνεδριάσεων, Τμήμα Μηχανολόγων Μηχανικών, Πανεπιστήμιο Θεσσαλίας 
Link1: 
files/sembouklas.pdf 
Link2: 

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