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	<title>Math Ziqi Wang</title>
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		<title>PINNs Introductory Code for the Heat Equation</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 01 Dec 2023 14:54:19 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Martín Hernández]]></category>
		<category><![CDATA[Hub Ziqi Wang]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Martin Hernandez]]></category>
		<category><![CDATA[Math Ziqi Wang]]></category>
		<category><![CDATA[controlling physical systems]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[RL]]></category>
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					<description><![CDATA[PINNs Introductory Code for the Heat Equation This repository provides some basic insights on Physics Informed Neural Networks (PINNs) and their implementation. PINNs are numerical methods based on the universal approximation capacity of neural networks, aiming to approximate solutions of partial differential equations. Recently, extensive focus has been on approximating solutions of various equations, leading [&#8230;]]]></description>
		
		
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		<title>Federated Learning: Protect your data and privacy</title>
		<link>https://dcn.nat.fau.eu/federated-learning-protect-your-data-and-privacy/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 20 Dec 2022 06:50:47 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Ziqi Wang]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Ziqi Wang]]></category>
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					<description><![CDATA[Federated Learning: Protect your data and privacy Code: A basic PyTorch implementation of the FedAvg algorithm (GitHub) Federated Learning is becoming an increasingly popular topic in machine learning. But what is it, and why do we need it? To explain what the excitement is all about, this post outlines the basics of Federated Learning and [&#8230;]]]></description>
		
		
		
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