Deformation approximation: Improve your Artificial Neural Network training using the Finite Element Method formulation. A case for static deformations
Speaker: Alban Odot (INRIA Strasbourg (MIMESIS-Inria), France)
Title: Deformation approximation: Improve your Artificial Neural Network training using the Finite Element Method formulation. A case for static deformations
Time: Wednesday, 2022.03.16, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Recently, with the increase in GPU computational power, deep learning started to revolutionise several fields, in particular computer vision, language processing, and image processing. Deep learning engineering can be split into three main categories : Dataset, Network Architecture and Learning policy. Setting the network architecture to its simplest form, we will modify the dataset and learning policy formulation using the Finite Element Method to improve the training.
The Finite Element Method is often used as the numerical method of reference for solving the PDE associated with non-linear object deformations. In order to solve the resulting energy minimisation equations, root-finding algorithms such as the Newton-Raphson method are used. During its iterative process, the Newton-Raphson reveals important information about the state of the system which can be used in both the dataset formulation and the training policy.