Machine Learning Seminar presentation

Topic: Data-Driven Hyper-elastic Simulations

Speaker: Saurabh Deshpande, University of Luxembourg, Department of Computational Science

Time: Wednesday 2020.10.14, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

Since a decade, machine learning has started to revolutionize several fields due to the development of new algorithms and the availability of more data every day. Deep learning, a class of machine learning methods based on learning data representations, has demonstrated strong abilities at extracting high-level representations of complex processes. In this work, we will implement a particular class of machine learning architecture called Convolutional Neural Network (CNN) to replace the finite element solver for 3D hyper-elastic simulations. Also, we will briefly touch upon the dropout technique and its possible use in predicting uncertainties of the neural network predictions.

Additional material:

1). Simulation of hyperelastic materials in real-time using Deep Learning – https://arxiv.org/abs/1904.06197

2) Dropout as Bayesian approximation – https://arxiv.org/abs/1506.02142