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	<title>physics-informed neural networks</title>
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		<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[Akademy]]></category>
		<category><![CDATA[Akademy Dania Sana]]></category>
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		<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>
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					<description><![CDATA[Approximating the 1D wave equation using Physics Informed Neural Networks (PINNs) Internship under the &#8220;Women in Mathematics of Data&#8221; program Date: September 2022 Supervisors: Enrique Zuazua Institution: FAU MoD, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Code: • See the complete report by Dania Sana &#160; Introduction Accurate and fast predictions of numerical solutions are of significant interest in [&#8230;]]]></description>
		
		
		
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