We probably want a tl;dr here, similar-ish in style to what Nils has here.
Welcome to the 3rd edition of the Introduction to Scientific Machine Learning for Engineers in the winter semester 2023/2024! We are looking forward to a hopefully great semester, and to excite as many of you as possible for Scientific Machine Learning.
The course breaks down into an introduction to the topic, followed by 4 core content blocks which are interspersed with practice problems while being supported by JuPyter notebook-based tutorials for the practical application of the learned concepts.
- Nikolaus A. Adams
- Artur Toshev (artur.toshev@tum.de)
Questions should preferably be posted in the Moodle, or else be sent to Artur Toshev.
Thanks to all contributors! Github names are provided in brackets where available.
- Armin Illerhaus - Notebooks on Windows
- Andreas Steger (AndSte01)
- Muhammet Ali Güldali
Please cite this work as:
@article{paehler2023sciml,
title={Introduction to Scientific Machine Learning for Engineers},
author={Ludger Paehler and Artur P Toshev and Nikolaus A Adams},
url={https://tumaer.github.io/SciML/about.html},
year={2023},
}:alt: tum-logo
:width: 200px
:align: center