update

content/07_hub/0_intro.md

@@ -28,7 +28,20 @@ The HUB brings together students and lecturers from different displicines in ord
 
 
 ## For Lecturers
-You want to publish your microcredit in our HUB? Great! Please send a short description in the required format (`to be defined`) to ki4allcluster@irmb.tu-bs.de.
+You want to publish your microcredit in our HUB? Great! Please send a short description in the required format shown below to ki4allcluster@irmb.tu-bs.de.
+
+```{code-block}
+## Name-of-microcredit
+
+Short description of microcredit (max. 700 characters).
+
+\`\`\`console
+git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+\`\`\`
+
+**Extent: 0.25/0.5/0.75/1 (ECTS??)**\
+**Responsible: Institute, University**
+````
 
 **Remark**: Please choose a licence file in your public git repository that you are referencing!
 

content/07_hub/1_programming_basics/python-intro-ifn.md

+## 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**
\ No newline at end of file

content/07_hub/2_ml_basics/genetic-algorithms-irmb.md

+## 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**

content/07_hub/2_ml_basics/ml-intro-ifn.md

+## 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**

content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md

+## PyTorch and Tensorflow 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**
\ No newline at end of file

content/07_hub/3_datadrivenmodeling.md

 # Data-driven modeling
 
+## DeepONet
+In this microcredit, you will be introduced to the concept of designing a deep neural network (DNN) for accurate `approximation of operators` which map input functions into output functions. These operators can be explicit or implicit types. In its simplest form an explicit operator could be a derivative or integral operator of any desired functions. A good example of implicit type would
+be the `solution operators of ordinary/partial differential equations` (ODEs/PDEs).
+
+
+```console
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git
+```
+**Extent: 1 ECTS**\
+**Responsible: iRMB, TU BS**
 ## Physics-Informed Neural Networks
 
 Neural networks are an exciting technique to solve a variety of scientific
@@ -15,21 +25,10 @@ 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
-Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional Bayesian update to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by identifying just three hyperparameters. This method, a type of physics-based regression, is particularly beneficial for online applications.
+Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional `Bayesian update` to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by `identifying just three hyperparameters`. This method, a type of `physics-based regression`, is particularly beneficial for `online applications`.
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone git@git.rz.tu-bs.de:irmb/teaching/microcredentials/statfem.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**
-
-## DeepONet
-In this microcredit, you will be introduced to the concept of designing a deep neural network (DNN) for accurate approximation of operators which map input functions into output functions. These operators can be explicit or implicit types. In its simplest form an explicit operator could be a derivative or integral operator of any desired functions. A good example of implicit type would
-be the solution operators of ordinary/partial differential equations (ODEs/PDEs).
-
-```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
\ No newline at end of file

content/07_hub/3_datadrivenmodeling/3_datadrivenmodeling.md

@@ -3,7 +3,7 @@ In this microcredit, you will be introduced to the concept of designing a deep n
 be the solution operators of ordinary/partial differential equations (ODEs/PDEs).
 
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**
@@ -23,9 +23,9 @@ 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
-Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional Bayesian update to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by identifying just three hyperparameters. This method, a type of physics-based regression, is particularly beneficial for online applications.
+Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional `Bayesian update` to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by `identifying just three hyperparameters`. This method, a type of `physics-based regression`, is particularly beneficial for `online applications`.
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone git@git.rz.tu-bs.de:irmb/teaching/microcredentials/statfem.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**

content/07_hub/3_datadrivenmodeling/deeponet.md

 ## DeepONet
-In this microcredit, you will be introduced to the concept of designing a deep neural network (DNN) for accurate approximation of operators which map input functions into output functions. These operators can be explicit or implicit types. In its simplest form an explicit operator could be a derivative or integral operator of any desired functions. A good example of implicit type would
-be the solution operators of ordinary/partial differential equations (ODEs/PDEs).
+In this microcredit, you will be introduced to the concept of designing a deep neural network (DNN) for accurate `approximation of operators` which map input functions into output functions. These operators can be explicit or implicit types. In its simplest form an explicit operator could be a derivative or integral operator of any desired functions. A good example of implicit type would
+be the `solution operators of ordinary/partial differential equations` (ODEs/PDEs).
 
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**

content/07_hub/3_datadrivenmodeling/statFEM.md

 ## Statistical Finite Element Method
-Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional Bayesian update to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by identifying just three hyperparameters. This method, a type of physics-based regression, is particularly beneficial for online applications.
+Nowadays, digital image correlation is used to measure strain and displacement, though it's often limited to accessible areas. To gauge inaccessible regions, constitutive models calibrated via conventional `Bayesian update` to infer full-field displacements and stress from sparse data. However, calibration accuracy can vary, particularly with aging structures. The recently proposed statistical Finite Element Method (statFEM) uses displacement as the stochastic prior, quantifying model-reality mismatch. It improves computational efficiency, reducing the need to solve partial differential equations online by `identifying just three hyperparameters`. This method, a type of `physics-based regression`, is particularly beneficial for `online applications`.
 ```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
+git clone git@git.rz.tu-bs.de:irmb/teaching/microcredentials/statfem.git
 ```
 **Extent: 1 ECTS**\
 **Responsible: iRMB, TU BS**