Surrogate Deep Learning Framework for Real-Time Hyper-Elastic Simulations with Uncertainties
Speaker: Saurabh Deshpande (Faculty of Science, Technology and Medicine; University of Luxembourg)
Title: Surrogate Deep Learning Framework for Real-Time Hyper-Elastic Simulations with Uncertainties
Time: Wednesday, 2021.09.29, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Conventional finite element solvers are computationally expensive to solve non-linear partial differential equations, particularly they perform poorly in real time scale applications. In this work we propose a deep learning surrogate model which predicts nonlinear displacement solutions for hyper-elastic constitutive models in real time. We implement the Bayesian inference approach, thereby giving probabilistic predictions of displacement fields, capable of giving uncertainty of predictions. We implement our framework to benchmark hyper-elastic simulations to prove that it is extremely fast yet accurate.