<?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>Reinforcement learning</title>
	<atom:link href="https://dcn.nat.fau.eu/tag/reinforcement-learning/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>Fri, 01 Dec 2023 15:23:48 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.0</generator>

<image>
	<url>https://dcn.nat.fau.eu/wp-content/uploads/cropped-FAUDCNAvHlogo_square_silhouette_512x512-32x32.png</url>
	<title>Reinforcement learning</title>
	<link>https://dcn.nat.fau.eu</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>PINNs Introductory Code for the Heat Equation</title>
		<link>https://dcn.nat.fau.eu/pinns-introductory-code-for-the-heat-equation/</link>
		
		<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>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=27768</guid>

					<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>
		
		
		<enclosure url="https://dcn.nat.fau.eu/wp-content/uploads/FAUDCNAvHpost_zWang_mHernandez_trainingSolConv.mp4" length="355115" type="video/mp4" />

			</item>
		<item>
		<title>The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach</title>
		<link>https://dcn.nat.fau.eu/the-admm-pinns-algorithmic-framework-for-nonsmooth-pde-constrained-optimization-a-deep-learning-approach/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 12 Jul 2023 09:00:42 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Yongcun Song]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Yongcun Song]]></category>
		<category><![CDATA[controlling physical systems]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[RL]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=26844</guid>

					<description><![CDATA[The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach Motivation This post shows the source code from the paper &#8220;The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach&#8221;. (See reference below) We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Reinforcement learning as a new perspective into controlling physical systems</title>
		<link>https://dcn.nat.fau.eu/reinforcement-learning-as-a-new-perspective-into-controlling-physical-systems/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 03 Jul 2023 03:43:26 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Theïlo Terrise]]></category>
		<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Theïlo Terrise]]></category>
		<category><![CDATA[controlling physical systems]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[RL]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=26794</guid>

					<description><![CDATA[Reinforcement learning as a new perspective into controlling physical systems Introduction Optimal control addresses the problem of bringing a system from an initial state to a target state, like a satellite that we want to send into orbit using the least possible amount of fuel. Since the last century, mathematics has helped develop powerful numerical [&#8230;]]]></description>
		
		
		
			</item>
	</channel>
</rss>
