Using Physics-Informed constrained neural network to reconstruct the motion of the particles for time more than the training time
Speaker: Fateme Darlik (Faculty of Science Technology and Medicine, University of Luxembourg)
Title: Using Physics-Informed constrained neural network to reconstruct the motion of the particles for time more than the training time
Time: Wednesday, 2022.06.22, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Physics-informed neural networks (PINNs) have been studied and used widely in the field of computational mechanics. In this study, we proposed a new approach to merge PINNs with (geometry-based) constrained neural networks. Using this method, we reconstruct the velocity fields of particles moving in the fixed bed. Following that, the trained neural network is used to predict the motion of particles for a period of time more than the training time, and the results are compared with the simulation data in terms of accuracy and CPU time.