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content/07_hub/0_intro.md

+# HUB
+
+The HUB provides a collection of teaching materials in the form of microcredits. A microcredit is a teaching unit that counts 0.25 - 1.0 ECTS. You may bundle a collection of different microcredits and bring them in as part of the interdisciplinary pool qualifications. 
+
+This could be the basic workflow:
+- Browse the below collection of microcredits
+- clone the exercise (jupyternotebook) onto the cluster (only for TU Braunschweig students with a valid GITZ ID!)
+- complete the assigments. 
+- If a report or oral examination is required, contact the responsible lecturer. 
+
+**Need help?** Contact your lecturers to recommend you tasks that match your individual level of knowledge and personal interests!
+
+The possible extent of the microcredits below is as follows:
+
+| ECTS            | 0.25       | 0.5        | 0.75       | 1.0        |
+| --------------- | ---------- | ---------- | ---------- | ---------- |
+| Working hours   | 6 - 7.5    | 12.5 - 15  | 18-22.5    | 25-30      |
+
+
+
+## Python introduction
+
+This microcredit introduces you to python programming. 
+
+Get the material from:
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+
+
+**Extent: 3 Microcredits (ECTS??)** \
+**Responsible: IFN, TU BS**
+
+
+## Machine Learning Introduction 
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+**Extent: 3 Microcredits (ECTS??)**\
+**Responsible: IFN, TU BS**
+
+## PyTorch and Tensorflow Introduction (3 Microcredits)
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+
+**Extent: 3 Microcredits (ECTS??)**\
+**Responsible: IFN, TU BS**
+
+## Physics-Informed Neural Networks
+
+Neural networks are an exciting technique to solve a variety of scientific
+problems. They are usually used in the data-driven regime. Less known is their
+applicability to `partial differential equations` (PDE), where they can
+be used to obtain solutions to boundary value problems directly without any
+data. This approach is called `physics informed neural networks` (PINN).
+In this small project, you will familiarize yourself with this approach and
+solve a simple steady-state heat equation.
+
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+
+## Statistical Finite Element Method
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+
+## Genetic Algorithms
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
+
+## Gaussian Processes
+Placeholder.
+```console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+```
+
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**