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		<id>http://www.colloquiam.com/wd/index.php?action=history&amp;feed=atom&amp;title=2023b</id>
		<title>2023b - Revision history</title>
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		<updated>2026-05-11T17:41:59Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276574&amp;oldid=prev</id>
		<title>JSanchez: JSanchez moved page Draft Sanchez Pinedo 591142824 to 2023b</title>
		<link rel="alternate" type="text/html" href="http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276574&amp;oldid=prev"/>
				<updated>2023-05-24T10:43:29Z</updated>
		
		<summary type="html">&lt;p&gt;JSanchez moved page &lt;a href=&quot;/public/Draft_Sanchez_Pinedo_591142824&quot; class=&quot;mw-redirect&quot; title=&quot;Draft Sanchez Pinedo 591142824&quot;&gt;Draft Sanchez Pinedo 591142824&lt;/a&gt; to &lt;a href=&quot;/public/2023b&quot; title=&quot;2023b&quot;&gt;2023b&lt;/a&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
				&lt;tr style='vertical-align: top;' lang='en'&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 10:43, 24 May 2023&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan='2' style='text-align: center;' lang='en'&gt;&lt;div class=&quot;mw-diff-empty&quot;&gt;(No difference)&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276527&amp;oldid=prev</id>
		<title>JSanchez at 10:28, 24 May 2023</title>
		<link rel="alternate" type="text/html" href="http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276527&amp;oldid=prev"/>
				<updated>2023-05-24T10:28:15Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
				&lt;col class='diff-marker' /&gt;
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				&lt;col class='diff-marker' /&gt;
				&lt;col class='diff-content' /&gt;
				&lt;tr style='vertical-align: top;' lang='en'&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 10:28, 24 May 2023&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l3&quot; &gt;Line 3:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 3:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;We consider a hybrid approach for the approximation of the solution to parametric partial differential equations based on finite elements and deep neural networks. Finite element simulations with adaptive mesh refinement are used to generate input data for the training of a neural network. A deep feedforward neural network is then used to approximate the solution of the partial differential equation. We aim at balancing the numerical errors introduced by the finite element method and the neural network approximation respectively. Numerical results are presented for the transport equation.&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;We consider a hybrid approach for the approximation of the solution to parametric partial differential equations based on finite elements and deep neural networks. Finite element simulations with adaptive mesh refinement are used to generate input data for the training of a neural network. A deep feedforward neural network is then used to approximate the solution of the partial differential equation. We aim at balancing the numerical errors introduced by the finite element method and the neural network approximation respectively. Numerical results are presented for the transport equation.&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;== Full Paper ==&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;lt;pdf&amp;gt;Media:Draft_Sanchez Pinedo_59114282417_file.pdf&amp;lt;/pdf&amp;gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;

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&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276525&amp;oldid=prev</id>
		<title>JSanchez at 10:28, 24 May 2023</title>
		<link rel="alternate" type="text/html" href="http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276525&amp;oldid=prev"/>
				<updated>2023-05-24T10:28:14Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
				&lt;col class='diff-marker' /&gt;
				&lt;col class='diff-content' /&gt;
				&lt;col class='diff-marker' /&gt;
				&lt;col class='diff-content' /&gt;
				&lt;tr style='vertical-align: top;' lang='en'&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 10:28, 24 May 2023&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l1&quot; &gt;Line 1:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 1:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;==Abstract==&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;We consider a hybrid approach for the approximation of the solution to parametric partial differential equations based on finite elements and deep neural networks. Finite element simulations with adaptive mesh refinement are used to generate input data for the training of a neural network. A deep feedforward neural network is then used to approximate the solution of the partial differential equation. We aim at balancing the numerical errors introduced by the finite element method and the neural network approximation respectively. Numerical results are presented for the transport equation.&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;

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&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276524&amp;oldid=prev</id>
		<title>JSanchez: Created blank page</title>
		<link rel="alternate" type="text/html" href="http://www.colloquiam.com/wd/index.php?title=2023b&amp;diff=276524&amp;oldid=prev"/>
				<updated>2023-05-24T10:28:12Z</updated>
		
		<summary type="html">&lt;p&gt;Created blank page&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

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