This course is a mathematical introduction to Scientific Machine Learning (SML). It introduces the theory of approximation: interpolation, linear and nonlinear regression, universal approximators, with a special focus on artificial neural networks (ANN). The concepts behind deep neural network will be explained. Another important topic of this course is the potential synergy between Mechanical modeling, Computer-assisted Engineering and Data-based Machine Learning approaches. Last but not least, this course is also intended to practically implement the methods and algorithms seen in the course. For that, modern development environments like Jupyter Notebooks with python as base programming language will be used.
- Responsable: De Vuyst Florian