From 401950e36fe060ee32de4661625cde75d78cf3ad Mon Sep 17 00:00:00 2001
From: Henning Wessels <h.wessels@tu-braunschweig.de>
Date: Wed, 7 Jun 2023 13:37:39 +0200
Subject: [PATCH] update
---
content/07_hub/0_intro.md | 57 -------------------
content/07_hub/1_programming_basics.md | 14 -----
.../1_programming_basics/python-intro-ifn.md | 12 ----
content/07_hub/2_ml_basics.md | 23 --------
.../2_ml_basics/gaussian-processes-irmb.md | 11 ----
.../2_ml_basics/genetic-algorithms-irmb.md | 10 ----
content/07_hub/2_ml_basics/ml-intro-ifn.md | 9 ---
.../2_ml_basics/pytorch-tf-intro-ifn.md | 9 ---
content/07_hub/3_datadrivenmodeling.md | 37 ------------
.../07_hub/3_datadrivenmodeling/deeponet.md | 11 ----
content/07_hub/3_datadrivenmodeling/pinn.md | 17 ------
.../07_hub/3_datadrivenmodeling/statFEM.md | 10 ----
12 files changed, 220 deletions(-)
delete mode 100644 content/07_hub/0_intro.md
delete mode 100644 content/07_hub/1_programming_basics.md
delete mode 100644 content/07_hub/1_programming_basics/python-intro-ifn.md
delete mode 100644 content/07_hub/2_ml_basics.md
delete mode 100644 content/07_hub/2_ml_basics/gaussian-processes-irmb.md
delete mode 100644 content/07_hub/2_ml_basics/genetic-algorithms-irmb.md
delete mode 100644 content/07_hub/2_ml_basics/ml-intro-ifn.md
delete mode 100644 content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md
delete mode 100644 content/07_hub/3_datadrivenmodeling.md
delete mode 100644 content/07_hub/3_datadrivenmodeling/deeponet.md
delete mode 100644 content/07_hub/3_datadrivenmodeling/pinn.md
delete mode 100644 content/07_hub/3_datadrivenmodeling/statFEM.md
diff --git a/content/07_hub/0_intro.md b/content/07_hub/0_intro.md
deleted file mode 100644
index a55323a..0000000
--- a/content/07_hub/0_intro.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# HUB
-
-`!!!! under construction !!!!`
-
-`features that need be added:`
-- `search for keywords`
-- `singularity files for colab for external users`
-- `clone only files`
-- `virtual spaces to foster exchange among students: link to StudIP? Blubber?`
-- `feedback to lecturers: comment function? StudIP group?`
-- `make lecturers contribute via git merge instead of email`
-- `translate pdfs from git repos into markdown`
-
-
-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:
-
-| ECTS | 0.25 | 0.5 | 0.75 | 1.0 |
-| --------------- | ---------- | ---------- | ---------- | ---------- |
-| Working hours | 6 - 7.5 | 12.5 - 15 | 18-22.5 | 25-30 |
-
-## For students
-You may bundle a collection of different microcredits and bring them in as part of the interdisciplinary pool qualifications.
-We invite you to ...
-- browse the provided collection of microcredits
-- clone the microcredits onto the cluster (only for TU Braunschweig students with a valid GITZ ID!) or any other machine
-- complete the assigments.
-- contact the responsible lecturer, if a report or assessment is required.
-
-**Need help?** Contact your lecturers to recommend you microcredits that match your individual level of knowledge and personal interests!
-
-```{figure} ../../figures/hub.png
----
-align: center
----
-The HUB brings together students and lecturers from different displicines in order to foster interdisciplinary education and research.
-```
-
-## For Lecturers
-You want to publish your microcredit in our HUB? Great! Please send a short description in the required [markdown](https://www.markdownguide.org) format shown below to ki4allcluster@irmb.tu-bs.de.
-
-```{code-block}
-## Name-of-microcredit
-
-Short description of microcredit (max. 700 characters) with highlighted `catchy keywords`.
-
-Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or
-\`\`\`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**
-```
-
-**Remarks**:
-- Please choose a licence file in your public git repository that you are referencing!
-- To ensure a smooth execution of any jupyternotebook on the TU BS Jupyterhub, please make sure that you have a suitable [python environment](https://irmb.gitlab-pages.rz.tu-bs.de/knowledge-base/content/04_cluster/2_jupyterhub.html) installed, that you refer to in your microcredit description.
diff --git a/content/07_hub/1_programming_basics.md b/content/07_hub/1_programming_basics.md
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index 38d07d4..0000000
--- a/content/07_hub/1_programming_basics.md
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-# Programming basics
-
-## Python introduction
-
-`Under construction`.
-
-Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or
-```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
diff --git a/content/07_hub/1_programming_basics/python-intro-ifn.md b/content/07_hub/1_programming_basics/python-intro-ifn.md
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index fbe1331..0000000
--- a/content/07_hub/1_programming_basics/python-intro-ifn.md
+++ /dev/null
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-## Python introduction
-
-`Under construction`.
-
-Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or
-```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
diff --git a/content/07_hub/2_ml_basics.md b/content/07_hub/2_ml_basics.md
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index 16c8ffe..0000000
--- a/content/07_hub/2_ml_basics.md
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-# Machine learning basics
-
-## Gaussian Processes
-
-Gaussian processes, also known as `Kriging`, are probabilistic models used in machine learning and statistics. They provide a flexible framework for modeling and `predicting functions or distributions`. A Gaussian is characterized by a `mean function and a covariance function (kernel)`, which determines the smoothness and correlation properties of the functions. Gaussian processes are widely used in regression, interpolation, and optimization tasks, providing uncertainty estimates along with predictions. They offer a `non-parametric approach` that can handle complex data and are particularly useful when limited data is available.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git
-```
-
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
-## Genetic Algorithms
-
-Genetic algorithms are `gradient-free optimization` techniques inspired by evolution. They start with a set of potential solutions represented as individuals. These individuals have genes (parameters) that encode solutions. Through selection, crossover, and mutation, new offspring are created. The fittest individuals have a higher chance of being selected. This process continues until a satisfactory solution is found. They are `useful for complex problems with many possible solutions`.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
diff --git a/content/07_hub/2_ml_basics/gaussian-processes-irmb.md b/content/07_hub/2_ml_basics/gaussian-processes-irmb.md
deleted file mode 100644
index 8792530..0000000
--- a/content/07_hub/2_ml_basics/gaussian-processes-irmb.md
+++ /dev/null
@@ -1,11 +0,0 @@
-## Gaussian Processes
-
-Gaussian processes, also known as `Kriging`, are probabilistic models used in machine learning and statistics. They provide a flexible framework for modeling and `predicting functions or distributions`. A Gaussian is characterized by a `mean function and a covariance function (kernel)`, which determines the smoothness and correlation properties of the functions. Gaussian processes are widely used in regression, interpolation, and optimization tasks, providing uncertainty estimates along with predictions. They offer a `non-parametric approach` that can handle complex data and are particularly useful when limited data is available.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/gaussian-processes.git
-```
-
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
diff --git a/content/07_hub/2_ml_basics/genetic-algorithms-irmb.md b/content/07_hub/2_ml_basics/genetic-algorithms-irmb.md
deleted file mode 100644
index b009e44..0000000
--- a/content/07_hub/2_ml_basics/genetic-algorithms-irmb.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## Genetic Algorithms
-
-Genetic algorithms are `gradient-free optimization` techniques inspired by evolution. They start with a set of potential solutions represented as individuals. These individuals have genes (parameters) that encode solutions. Through selection, crossover, and mutation, new offspring are created. The fittest individuals have a higher chance of being selected. This process continues until a satisfactory solution is found. They are `useful for complex problems with many possible solutions`.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/genetic-algorithms.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
diff --git a/content/07_hub/2_ml_basics/ml-intro-ifn.md b/content/07_hub/2_ml_basics/ml-intro-ifn.md
deleted file mode 100644
index c57fa2e..0000000
--- a/content/07_hub/2_ml_basics/ml-intro-ifn.md
+++ /dev/null
@@ -1,9 +0,0 @@
-## Machine Learning Introduction
-`Under construction`.
-
-Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or
-```console
-git clone https://git.rz.tu-bs.de/my-name-space/my-repo.git
-```
-**Extent: 3 Microcredits (ECTS??)**\
-**Responsible: IFN, TU BS**
diff --git a/content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md b/content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md
deleted file mode 100644
index e901a9d..0000000
--- a/content/07_hub/2_ml_basics/pytorch-tf-intro-ifn.md
+++ /dev/null
@@ -1,9 +0,0 @@
-## PyTorch and Tensorflow Introduction
-`Under construction`.
-
-Check out https://git.rz.tu-bs.de/my-name-space/my-repo.git or
-```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
diff --git a/content/07_hub/3_datadrivenmodeling.md b/content/07_hub/3_datadrivenmodeling.md
deleted file mode 100644
index 5db90a4..0000000
--- a/content/07_hub/3_datadrivenmodeling.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# 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).
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git or
-```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
-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.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.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`.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/statfem.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/statfem.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
diff --git a/content/07_hub/3_datadrivenmodeling/deeponet.md b/content/07_hub/3_datadrivenmodeling/deeponet.md
deleted file mode 100644
index 3f715e8..0000000
--- a/content/07_hub/3_datadrivenmodeling/deeponet.md
+++ /dev/null
@@ -1,11 +0,0 @@
-## 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).
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/deeponet.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**\
-**Written by: [Hesameddin Safari]()**
diff --git a/content/07_hub/3_datadrivenmodeling/pinn.md b/content/07_hub/3_datadrivenmodeling/pinn.md
deleted file mode 100644
index b075ac7..0000000
--- a/content/07_hub/3_datadrivenmodeling/pinn.md
+++ /dev/null
@@ -1,17 +0,0 @@
-## 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.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/physics-informed-neural-networks.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**
-**Written by: [Alexander Henkes](https://orcid.org/0000-0003-4615-9271)**
\ No newline at end of file
diff --git a/content/07_hub/3_datadrivenmodeling/statFEM.md b/content/07_hub/3_datadrivenmodeling/statFEM.md
deleted file mode 100644
index 836ea7a..0000000
--- a/content/07_hub/3_datadrivenmodeling/statFEM.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## 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`.
-
-Check out https://git.rz.tu-bs.de/irmb/teaching/microcredentials/statfem.git or
-```console
-git clone https://git.rz.tu-bs.de/irmb/teaching/microcredentials/statfem.git
-```
-**Extent: 1 ECTS**\
-**Responsible: iRMB, TU BS**\
-**Written by: [Vahab Narouie](https://orcid.org/0000-0003-1410-4671)**
--
GitLab