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updating week 42

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Morten Hjorth-Jensen
2021-10-20 08:33:02 +02:00
parent 3622b9bf3d
commit a72134c6c6
9 changed files with 77 additions and 7 deletions
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doc/pub/week42/html/._week42-bs000.html
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@@ -368,7 +368,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Oct 19, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week42/html/._week42-bs003.html
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@@ -352,9 +352,24 @@ MathJax.Hub.Config({
<h2 id="solving-odes-with-deep-learning" class="anchor">Solving ODEs with Deep Learning </h2>
<p>
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
The Universal Approximation Theorem states that a neural network can
approximate any function at a single hidden layer along with one input
and output layer to any given precision.
</div>
</div>
<p>
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<a href="https://www.springer.com/gp/book/9789401798150" target="_self">An Introduction to Neural Network Methods for Differential Equations</a>, by Yadav and Kumar.
</div>
</div>
<p>
<p>
doc/pub/week42/html/week42-bs.html
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@@ -368,7 +368,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Oct 19, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week42/html/week42-reveal.html
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Oct 19, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
@@ -200,10 +200,21 @@ we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5q
<section>
<h2 id="solving-odes-with-deep-learning">Solving ODEs with Deep Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
The Universal Approximation Theorem states that a neural network can
approximate any function at a single hidden layer along with one input
and output layer to any given precision.
</div>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b>Book on solving differential equations with ML methods</b>
<p>
<a href="https://www.springer.com/gp/book/9789401798150" target="_blank">An Introduction to Neural Network Methods for Differential Equations</a>, by Yadav and Kumar.
</div>
</section>
doc/pub/week42/html/week42-solarized.html
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@@ -283,7 +283,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Oct 19, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -335,10 +335,23 @@ we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5q
<h2 id="solving-odes-with-deep-learning">Solving ODEs with Deep Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
The Universal Approximation Theorem states that a neural network can
approximate any function at a single hidden layer along with one input
and output layer to any given precision.
</div>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b>Book on solving differential equations with ML methods</b>
<p>
<a href="https://www.springer.com/gp/book/9789401798150" target="_blank">An Introduction to Neural Network Methods for Differential Equations</a>, by Yadav and Kumar.
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week42/html/week42.html
+14 -1
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@@ -288,7 +288,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Oct 19, 2021</h4></center> <!-- date -->
<center><h4>Oct 20, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -340,10 +340,23 @@ we will also study the usage of <a href="https://www.youtube.com/watch?v=fRf4l5q
<h2 id="solving-odes-with-deep-learning">Solving ODEs with Deep Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
The Universal Approximation Theorem states that a neural network can
approximate any function at a single hidden layer along with one input
and output layer to any given precision.
</div>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b>Book on solving differential equations with ML methods</b>
<p>
<a href="https://www.springer.com/gp/book/9789401798150" target="_blank">An Introduction to Neural Network Methods for Differential Equations</a>, by Yadav and Kumar.
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
doc/pub/week42/ipynb/ipynb-week42-src.tar.gz
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doc/pub/week42/ipynb/week42.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Oct 19, 2021**\n",
"Date: **Oct 20, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -40,6 +40,8 @@
"\n",
"\n",
"\n",
"\n",
"\n",
"## Using Automatic differentiation\n",
"\n",
"In our discussions of ordinary differential equations \n",
@@ -50,7 +52,15 @@
"\n",
"The Universal Approximation Theorem states that a neural network can\n",
"approximate any function at a single hidden layer along with one input\n",
"and output layer to any given precision. \n",
"and output layer to any given precision.\n",
"\n",
"\n",
"\n",
"**Book on solving differential equations with ML methods.**\n",
"\n",
"[An Introduction to Neural Network Methods for Differential Equations](https://www.springer.com/gp/book/9789401798150), by Yadav and Kumar.\n",
"\n",
"\n",
"\n",
"\n",
"## Ordinary Differential Equations\n",
doc/src/week42/week42.do.txt
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@@ -21,6 +21,8 @@ DATE: today
* "Video on Convolutional Neural Networks from MIT":"https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini"
!eblock
!split
===== Using Automatic differentiation =====
@@ -31,9 +33,15 @@ we will also study the usage of "Autograd":"https://www.youtube.com/watch?v=fRf4
!split
===== Solving ODEs with Deep Learning =====
!bblock
The Universal Approximation Theorem states that a neural network can
approximate any function at a single hidden layer along with one input
and output layer to any given precision.
!eblock
!bblock Book on solving differential equations with ML methods
"An Introduction to Neural Network Methods for Differential Equations":"https://www.springer.com/gp/book/9789401798150", by Yadav and Kumar.
!eblock
!split