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
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.
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