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Morten Hjorth-Jensen
2021-11-15 08:42:38 +01:00
parent 6e6a5c152c
commit 49e22e6ab3
9 changed files with 56 additions and 56 deletions
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doc/Projects/2021/Project3/html/._Project3-bs000.html
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@@ -160,7 +160,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 14, 2021</h4>
<h4>Nov 15, 2021</h4>
</center> <!-- date -->
<br>
@@ -192,7 +192,7 @@ MathJax.Hub.Config({
<p>We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">from week 43</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_self">Yadav et al</a>.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">from week 42</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_self">Yadav et al</a>.</p>
<p><b>Note</b>: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise. </p>
<h2 id="the-basic-structure-of-your-project" class="anchor">The basic structure of your project </h2>
@@ -222,7 +222,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_self">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" target="_self">Kristine Baluka Hein</a> at this course are highly recommended.</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_self">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">Kristine Baluka Hein and included in the lectures of week 42</a> at this course are highly recommended.</p>
<p>For the machine learning part you can use your own code from project 2 or the functionality of for example <b>Tensorflow/Keras</b>.. </p>
<h3 id="part-a-setting-up-the-problem" class="anchor">Part a), setting up the problem </h3>
doc/Projects/2021/Project3/html/Project3-bs.html
+3 -3
View File
@@ -160,7 +160,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 14, 2021</h4>
<h4>Nov 15, 2021</h4>
</center> <!-- date -->
<br>
@@ -192,7 +192,7 @@ MathJax.Hub.Config({
<p>We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">from week 43</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_self">Yadav et al</a>.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">from week 42</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_self">Yadav et al</a>.</p>
<p><b>Note</b>: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise. </p>
<h2 id="the-basic-structure-of-your-project" class="anchor">The basic structure of your project </h2>
@@ -222,7 +222,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_self">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" target="_self">Kristine Baluka Hein</a> at this course are highly recommended.</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_self">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">Kristine Baluka Hein and included in the lectures of week 42</a> at this course are highly recommended.</p>
<p>For the machine learning part you can use your own code from project 2 or the functionality of for example <b>Tensorflow/Keras</b>.. </p>
<h3 id="part-a-setting-up-the-problem" class="anchor">Part a), setting up the problem </h3>
doc/Projects/2021/Project3/html/Project3.html
+3 -3
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@@ -194,7 +194,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 14, 2021</h4>
<h4>Nov 15, 2021</h4>
</center> <!-- date -->
<br>
<h1 id="paths-for-project-3">Paths for project 3 </h1>
@@ -223,7 +223,7 @@ MathJax.Hub.Config({
<p>We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_blank">from week 43</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_blank">Yadav et al</a>.</p>
<p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_blank">from week 42</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_blank">Yadav et al</a>.</p>
<p><b>Note</b>: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise. </p>
<h2 id="the-basic-structure-of-your-project">The basic structure of your project </h2>
@@ -253,7 +253,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_blank">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" target="_blank">Kristine Baluka Hein</a> at this course are highly recommended.</p>
<p>For the explicit scheme, you can study for example chapter 10 of the lecture notes in <a href="https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" target="_blank">Computational Physics</a> or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_blank">Kristine Baluka Hein and included in the lectures of week 42</a> at this course are highly recommended.</p>
<p>For the machine learning part you can use your own code from project 2 or the functionality of for example <b>Tensorflow/Keras</b>.. </p>
<h3 id="part-a-setting-up-the-problem">Part a), setting up the problem </h3>
doc/Projects/2021/Project3/ipynb/Project3.ipynb
+39 -39
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "e6c08da8",
"id": "2a5e5636",
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@@ -14,7 +14,7 @@
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@@ -22,14 +22,14 @@
"# Project 3 on Machine Learning, deadline December 17, 2021\n",
"**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
"Date: **Nov 14, 2021**\n",
"Date: **Nov 15, 2021**\n",
"\n",
"Copyright 1999-2021, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license"
]
},
{
"cell_type": "markdown",
"id": "cedebbe4",
"id": "6ce728c3",
"metadata": {
"editable": true
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@@ -39,7 +39,7 @@
},
{
"cell_type": "markdown",
"id": "9038e49a",
"id": "72bbcc49",
"metadata": {
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},
@@ -72,14 +72,14 @@
"\n",
"We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.\n",
"\n",
"This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides [from week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150).\n",
"This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides [from week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150).\n",
"\n",
"**Note**: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise."
]
},
{
"cell_type": "markdown",
"id": "89190c80",
"id": "b830757c",
"metadata": {
"editable": true
},
@@ -91,7 +91,7 @@
},
{
"cell_type": "markdown",
"id": "b2944795",
"id": "d8414014",
"metadata": {
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@@ -103,7 +103,7 @@
},
{
"cell_type": "markdown",
"id": "5d7f5f66",
"id": "b34cd53b",
"metadata": {
"editable": true
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@@ -115,7 +115,7 @@
},
{
"cell_type": "markdown",
"id": "55083a27",
"id": "f1a8c11e",
"metadata": {
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@@ -127,7 +127,7 @@
},
{
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"metadata": {
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@@ -139,7 +139,7 @@
},
{
"cell_type": "markdown",
"id": "99cbbdf4",
"id": "c45d9ea8",
"metadata": {
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@@ -151,7 +151,7 @@
},
{
"cell_type": "markdown",
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"id": "0dbde4e8",
"metadata": {
"editable": true
},
@@ -164,14 +164,14 @@
"equation in one dimension using a standard explicit scheme and neural\n",
"networks to solve the same equations.\n",
"\n",
"For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein](https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html) at this course are highly recommended.\n",
"For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein and included in the lectures of week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) at this course are highly recommended.\n",
"\n",
"For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**.."
]
},
{
"cell_type": "markdown",
"id": "c76618e8",
"id": "09596115",
"metadata": {
"editable": true
},
@@ -185,7 +185,7 @@
},
{
"cell_type": "markdown",
"id": "ac2c5732",
"id": "cda687af",
"metadata": {
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@@ -197,7 +197,7 @@
},
{
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"id": "a55bb07a",
"metadata": {
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@@ -207,7 +207,7 @@
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@@ -219,7 +219,7 @@
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@@ -229,7 +229,7 @@
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@@ -241,7 +241,7 @@
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{
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"metadata": {
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@@ -252,7 +252,7 @@
},
{
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"id": "2df03aa0",
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@@ -264,7 +264,7 @@
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@@ -274,7 +274,7 @@
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@@ -286,7 +286,7 @@
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@@ -299,7 +299,7 @@
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@@ -311,7 +311,7 @@
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@@ -321,7 +321,7 @@
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@@ -333,7 +333,7 @@
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{
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"id": "107cf59e",
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@@ -343,7 +343,7 @@
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@@ -355,7 +355,7 @@
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@@ -366,7 +366,7 @@
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"id": "9e4e7daf",
"metadata": {
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@@ -382,7 +382,7 @@
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@@ -399,7 +399,7 @@
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@@ -415,7 +415,7 @@
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@@ -427,7 +427,7 @@
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@@ -462,7 +462,7 @@
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@@ -493,7 +493,7 @@
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@@ -515,7 +515,7 @@
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doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz
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doc/Projects/2021/Project3/pdf/Project3.p.tex
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@@ -149,7 +149,7 @@ Project 3 on Machine Learning, deadline December 17, 2021
% --- begin date ---
\begin{center}
Nov 14, 2021
Nov 15, 2021
\end{center}
% --- end date ---
@@ -195,7 +195,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 43} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
\textbf{Note}: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -226,7 +226,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html}}{Kristine Baluka Hein} at this course are highly recommended.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{Kristine Baluka Hein and included in the lectures of week 42} at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example \textbf{Tensorflow/Keras}..
doc/Projects/2021/Project3/pdf/Project3.pdf
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doc/Projects/2021/Project3/pdf/Project3.tex
+3 -3
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@@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 17, 2021
% --- begin date ---
\begin{center}
Nov 14, 2021
Nov 15, 2021
\end{center}
% --- end date ---
@@ -169,7 +169,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 43} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
\textbf{Note}: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -200,7 +200,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html}}{Kristine Baluka Hein} at this course are highly recommended.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{Kristine Baluka Hein and included in the lectures of week 42} at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example \textbf{Tensorflow/Keras}..
doc/src/Projects/2021/Project3/Project3.do.txt
+2 -2
View File
@@ -31,7 +31,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides "from week 43":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" and/or the textbook by "Yadav et al":"https://www.springer.com/gp/book/9789401798150".
This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides "from week 42":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" and/or the textbook by "Yadav et al":"https://www.springer.com/gp/book/9789401798150".
_Note_: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -69,7 +69,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in "Computational Physics":"https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by "Kristine Baluka Hein":"https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" at this course are highly recommended.
For the explicit scheme, you can study for example chapter 10 of the lecture notes in "Computational Physics":"https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by "Kristine Baluka Hein and included in the lectures of week 42":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example _Tensorflow/Keras_..