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	<description>FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
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		<title>FAU MoD Lecture: A data-driven approach to closed-loop control of wound state progression to drive healing outcomes</title>
		<link>https://dcn.nat.fau.eu/fau-mod-lecture-a-data-driven-approach-to-closed-loop-control-of-wound-state-progression-to-drive-healing-outcomes/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 21 Apr 2026 11:13:27 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
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					<description><![CDATA[Date: Mon. May 4, 2026 Event: FAU MoD Lecture Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) FAU MoD Lecture: A data-driven approach to closed-loop control of wound state progression to drive healing outcomes Speaker: Prof. Dr. Marcella M. Gomez Affiliation: University of California, Santa Cruz (USA) Abstract. [&#8230;]]]></description>
		
		
		
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		<title>MLPDES26, Machine Learning and PDEs Workshop (2026)</title>
		<link>https://dcn.nat.fau.eu/mlpdes26/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 20 Apr 2026 18:20:22 +0000</pubDate>
				<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Enrique Zuazua]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[EZuazua Events]]></category>
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					<description><![CDATA[Date: Mon.-Wed. June 22 &#8211; 24, 2026 Event: MLPDES26, Machine Learning and PDEs Workshop (2026) Host: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Event type: Hybrid (On-site / Online) Registration form: dcn.nat.fau.eu/registration-mlpdes26 Next Spring, our FAU MoD, Research Center for Mathematics of Data is hosting the &#8220;Machine Learning and [&#8230;]]]></description>
		
		
		
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		<title>FAU MoD Lecture: Data Driven Modeling for Scientific Discovery and Digital Twins</title>
		<link>https://dcn.nat.fau.eu/fau-mod-lecture-data-driven-modeling-for-scientific-discovery-and-digital-twins/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 20 Apr 2026 11:13:07 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
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					<description><![CDATA[Date: Mon. April 20, 2026 Event: FAU MoD Lecture Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) FAU MoD Lecture: Data Driven Modeling for Scientific Discovery and Digital Twins Speaker: Prof. Dr. Dongbin Xiu Affiliation: Department of Mathematics. The Ohio State University (USA) Abstract. We present a data-driven [&#8230;]]]></description>
		
		
		
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		<title>Optimal control for renormalized solutions of nonlinear evolution equations</title>
		<link>https://dcn.nat.fau.eu/optimal-control-for-renormalized-solutions-of-nonlinear-evolution-equations/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 01 Apr 2026 01:00:10 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Pedro Blöss]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32695</guid>

					<description><![CDATA[&#160; 1 Introduction We aim to develop a comprehensive theory of optimal control for nonlinear parabolic equations of Leray-Lions type, whose solutions may fail to exist in the classical weak sense for low regularity data. In this case, the appropriate notion of solution is the renormalized solution, introduced by Lions and Di Perna [8] for [&#8230;]]]></description>
		
		
		
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		<title>BNU &#8211; Control and Machine Learning: A Mathematical Journey</title>
		<link>https://dcn.nat.fau.eu/bnu-control-and-machine-learning-a-mathematical-journey/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 20 Mar 2026 13:56:53 +0000</pubDate>
				<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Events]]></category>
		<category><![CDATA[News]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32557</guid>

					<description><![CDATA[Event: Beijing Normal University Mathematics Forum Date: Thu. March 19, 2026 Organizer: Beijing Normal University Lecture: Control and Machine Learning: A Mathematical Journey Speaker: Prof. Enrique Zuazua, FAU &#8211; Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) &#160; Enrique Zuazua Explores the Intersection of Control Theory and AI at Beijing Normal University On March 19, 2026, Prof. Enrique Zuazua presented [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Network design and control: Shape and topology optimization for the turnpike property for the wave equation</title>
		<link>https://dcn.nat.fau.eu/network-design-and-control-shape-and-topology-optimization-for-the-turnpike-property-for-the-wave-equation/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 17 Mar 2026 17:13:40 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Jan Sokolowski]]></category>
		<category><![CDATA[Math Martin Gugat]]></category>
		<category><![CDATA[Math Meizhi Qian]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32521</guid>

					<description><![CDATA[Network design and control: Shape and topology optimization for the turnpike property for the wave equation &#160; 1 Introduction We consider two optimal control problems. The first problem, denoted by , is an optimal control problem governed by an evolution equation. The second problem, denoted by , is the optimal control problem for the associated [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Reinforcement Learning and LQR with special control structure: switched and multilevel systems</title>
		<link>https://dcn.nat.fau.eu/reinforcement-learning-and-lqr-with-special-control-structure-switched-and-multilevel-systems/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 16 Mar 2026 21:31:41 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Nicolas Schlosser]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32509</guid>

					<description><![CDATA[Reinforcement Learning and LQR with special control structure: switched and multilevel systems &#160; 1 Introduction In this post we study the well-known linear-quadratic regulator problem in continuous time where , , , , and is the initial state. The goal is to choose a control in such a way that at time , we have [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Exact Controllability of Stochastic First-Order Multi-Dimensional Hyperbolic Systems</title>
		<link>https://dcn.nat.fau.eu/exact-controllability-of-stochastic-first-order-multi-dimensional-hyperbolic-systems/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 11 Mar 2026 04:01:31 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Yu Wang]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32466</guid>

					<description><![CDATA[Exact Controllability of Stochastic First-Order Multi-Dimensional Hyperbolic Systems In the real world, the evolution of many physical quantities can be described by first-order hyperbolic systems. Notable examples include the Saint-Venant equations for open channels, the Aw-Rascle model for road traffic, gas dynamics, and shallow water equations. Over the past few decades, the boundary control theory [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Course: Introduction to Control and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/course-introduction-to-control-and-machine-learning/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 10 Mar 2026 22:55:33 +0000</pubDate>
				<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Enrique Zuazua]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[EZuazua Events]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32471</guid>

					<description><![CDATA[Introduction to Control and Machine Learning Master Course – FAU, Friedrich-Alexander-Universität Erlangen–Nürnberg This course explores the deep connections between control theory, dynamical systems, and modern machine learning, highlighting how mathematical tools developed for the analysis of differential equations can help understand and design modern AI systems. Course Overview The course introduces the mathematical foundations of [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Optimal Output Regulation for General Linear Systems via Adaptive Dynamic Programming</title>
		<link>https://dcn.nat.fau.eu/optimal-output-regulation-for-general-linear-systems-via-adaptive-dynamic-programming/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 27 Feb 2026 15:13:32 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Yanzhi Wu]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32426</guid>

					<description><![CDATA[&#160; 1 Introduction In this study, we consider an adaptive optimal output regulation problem for general linear systems. The purpose is to obtain both optimal feedback control gain and optimal feedforward control gain, which appear in the optimal controller and can help realize asymptotic and disturbance rejection. First, adaptive dynamic programming technique is used to [&#8230;]]]></description>
		
		
		
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