Machine Learning Seminar presentation
Topic: Non-parametric data-driven constitutive modelling using artificial neural networks.
Speaker: Vu Chau, Department of Engineering, FSTM, University of Luxembourg
Time: Wednesday, 2022.04.13, 10:00 CET
How to join: Please contact Jakub Lengiewicz
The presentation investigates certain physics-motivated consistency requirements (e.g. limit behaviour, monotonicity) for the ANN-based prediction of principal stresses for given principal stretches, and discusses the implications on the architecture of such constitutive ANNs. The neural network is exemplarily constructed, trained and tested using PyTorch.
The computational embedding of the data-driven material descriptor is demonstrated for the open-source finite element framework FEniCS which builds on the symbolic representation of the constitutive ANN operator in the Unified Form Language (UFL). We discuss the performance of the overall formulation within the non-linear solution process and will explain some future directions of research.
Video recording: https://youtu.be/Vn0fLGHAkbk