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Machine Learning Tutorial

Numerical examples for regression and classification of data

Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.

Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.

Jupyter notebooks on Binder

The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder

https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git

Installation

To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:

  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.

  1. Change the working directory
$ cd ML-Tutorial
  1. Create a conda environment
$ conda env create -f environment.yml
  1. Activate the environment
$ conda activate ml-tutorial
  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation

If you want to remove the tutorial from your hardware, you need to follow those steps:

  1. Deactivate the conda environment
$ conda deactivate
  1. Remove the environment
$ conda env remove -n ml-tutorial
  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies

The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:

  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)

License

The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under the conditions of the GNU General Public License (GPLv3)

The contents of notebooks and documents are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)

About

Applications of Machine Learning in Mechanics of Materials

Resources

Readme
GPL-3.0 license
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Stars

4 stars

Watchers

1 watching

Forks

1 fork
Report repository

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Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
About
Applications of Machine Learning in Mechanics of Materials
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Machine Learning Tutorial
Numerical examples for regression and classification of data
Machine learning methods are trained with different data sets to work either as regression functions or to find the delimiter lines between classes of data points with different characteristics. Furthermore, it is demonstrated how a surrogate model can be built by supervised learning.
Additionally, a full chain of tutorials has been added to demosntrate (i) how the anisotropic elastic properties of composite material can be determined by finite element analysis, (ii) how consistent data sets on the microstructural features (volume fraction and geometrical arrangement of filler phase) and the resulting elastic properties can be generated, and (iii) how different machine learning algorithms can be trained on such data.
Jupyter notebooks on Binder
The tutorial is conveniently used with Jupyter notebooks that can be directly accessed with Binder:
Binder
https://mybinder.org/v2/gh/AHartmaier/ML-Tutorial.git
Installation
To use the tutorial on your own hardware, you need an Anaconda or Miniconda installation with a recent Python version. Then follow those steps:
  1. Download the contents of the GitHub repository, e.g. with
$ git clone https://github.com/AHartmaier/ML-Tutorial.git

or download and unpack the ZIP archive directly from GitHub.
  1. Change the working directory
$ cd ML-Tutorial

  1. Create a conda environment
$ conda env create -f environment.yml

  1. Activate the environment
$ conda activate ml-tutorial

  1. Start JupyterLab (or juypter notebook)
$ jupyter lab index.ipynb

De-Installation
If you want to remove the tutorial from your hardware, you need to follow those steps:
  1. Deactivate the conda environment
$ conda deactivate

  1. Remove the environment
$ conda env remove -n ml-tutorial

  1. Delete the folder ML-Tutorial
$ cd ..; rm -rf ML-Tutorial

Dependencies
The tutorial uses the following packages, which are automatically installed in the environment when following the instruction above:
  • NumPy for array handling and mathematical operations
  • scikit-learn for machine learning algorithms
  • MatPlotLib for graphical output
  • ipympl defining widgets for user interaction
  • pylabfea for finite element analysis (FEA)
License
The software in this tutorial comes with ABSOLUTELY NO WARRANTY. This is free
software, and you are welcome to redistribute it under the conditions of
the GNU General Public License
(GPLv3)
The contents of notebooks and documents are published under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
(CC BY-NC-SA 4.0)
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Applications of Machine Learning in Mechanics of Materials
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