Artificial Intelligence in Computational Mechanics
- Master's elective course - summer semester - 4 SWS - 6 ECTS
- Scheduled Tuesdays 9:45 - 13:00
Objective
- A fundamental understanding of artificial intelligence with a focus on machine learning
- Basic implementational skills within Python and Pytorch
- The ability to identify possible applications of artificial intelligence in the field of computational
mechanics
Content
Introduction to Machine Learning
- Fundamental Concepts in Machine Learning
- Neural Networks
Machine Learning in Physics and Engineering
- Physics-Informed Neural Networks (PINNs)
- Deep Energy Method
- Neural Network-based Surrogate Models
- Inverse Problems
- Singular Value Decomposition (SVD)
- Reduced Order Models (ROM)
- Sparse Identification of Nonlinear Dynamics (SINDy)
Recommended Reading
- Kollmannsberger, S., Davide, D., Jokeit, M. Herrmann, L., Deep Learning in Computational Mechanics, Springer, 2021
- Brunton, S. L., Kutz, J. N., Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, 2019
Prerequisites
- Fundamental programming skills, e.g. Computation in Engineering 1