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		<title>Null controllability for population dynamics with age, size structuring and diffusion</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 27 Jun 2023 17:26:25 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Yacouba Simpore]]></category>
		<category><![CDATA[boundary controllability]]></category>
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					<description><![CDATA[Null controllability for population dynamics with age, size structuring and diffusion 1. Motivation and description of age, size structured model 1.1. Motivation Knowing the mechanisms of tumor growth can be useful for developing treatments. To mathematically describe its evolution from a global point of view, we can use a so-called &#8220;diffusion&#8221; equation. Diffusion equations are [&#8230;]]]></description>
		
		
		
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		<title>Breaking the symmetry with Robin boundary conditions</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 03 May 2023 18:13:03 +0000</pubDate>
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		<category><![CDATA[Math Alba Lia Masiello]]></category>
		<category><![CDATA[boundary controllability]]></category>
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					<description><![CDATA[Breaking the symmetry with Robin boundary conditions Introduction Establishing symmetry properties of solutions to differential equations is a very important task in mathematical analysis, both from the theoretical point of view and for applications. Indeed, partial differential equations arise in modeling many phenomena in physics, mechanics, and so on. A first question that arises quite [&#8230;]]]></description>
		
		
		
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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>
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		<category><![CDATA[Hub Dania Sana]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Dania Sana]]></category>
		<category><![CDATA[boundary controllability]]></category>
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		<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) &#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>
		
		
		
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