<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>wave equation</title>
	<atom:link href="https://dcn.nat.fau.eu/tag/wave-equation/feed/" rel="self" type="application/rss+xml" />
	<link>https://dcn.nat.fau.eu</link>
	<description>FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
	<lastBuildDate>Thu, 27 Jul 2023 11:21:39 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.9.4</generator>

<image>
	<url>https://dcn.nat.fau.eu/wp-content/uploads/cropped-FAUDCNAvHlogo_square_silhouette_512x512-32x32.png</url>
	<title>wave equation</title>
	<link>https://dcn.nat.fau.eu</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs)</title>
		<link>https://dcn.nat.fau.eu/approximating-the-1d-wave-equation-using-physics-informed-neural-networks-pinns/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 30 Sep 2022 10:59:13 +0000</pubDate>
				<category><![CDATA[All]]></category>
		<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Dania Sana]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Dania Sana]]></category>
		<category><![CDATA[boundary controllability]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[parameter identification]]></category>
		<category><![CDATA[physics-informed neural networks]]></category>
		<category><![CDATA[wave equation]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=22800</guid>

					<description><![CDATA[Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs) &#160; Introduction Accurate and fast predictions of numerical solutions are of significant interest in many areas of science and industry. On one hand, most theoretical methods used in the industry are the result of deriving differential equations that are based on conservation laws, physical [&#8230;]]]></description>
		
		
		
			</item>
	</channel>
</rss>
