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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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	<item>
		<title>Enrique Zuazua invited to deliver a Special Section Lecture at ICM 2026</title>
		<link>https://dcn.nat.fau.eu/icm2026-enrique-zuazua-lecture/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 15 Jul 2026 11:13:34 +0000</pubDate>
				<category><![CDATA[EZuazua]]></category>
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		<category><![CDATA[News]]></category>
		<category><![CDATA[fau]]></category>
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		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32410</guid>

					<description><![CDATA[Date: July 23 – 30, 2026 Event: ICM 2026. Philadelphia, Pennsylvania (USA) Lecture: Machine Learning and Control: Foundations, Advances, and Perspectives Speaker: Prof. Enrique Zuazua, FAU &#8211; Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Enrique Zuazua honored with a prestigious speaker invitation to deliver a Special Section Lecture on Control Theory at the International Congress of Mathematicians 2026 (ICM 2026) [&#8230;]]]></description>
		
		
		
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		<item>
		<title>From Optimal Control to Reinforcement Learning: A Motivating Example from the Perspective of Linear Quadratic Regulators</title>
		<link>https://dcn.nat.fau.eu/from-optimal-control-to-reinforcement-learning/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 14 Jul 2026 20:42:48 +0000</pubDate>
				<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Fredson Silva]]></category>
		<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Fredson Silva]]></category>
		<category><![CDATA[Control]]></category>
		<category><![CDATA[Reinforcement learning]]></category>
		<category><![CDATA[RL]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33205</guid>

					<description><![CDATA[From Optimal Control to Reinforcement Learning: A Motivating Example from the Perspective of Linear Quadratic Regulators Fredson Silva De Souza Aguiar, Visiting Master Student. University of Sorbonne Paris Nord Repository with code and trained models available at FAU DCN-AvH GitHub: https://github.com/DCN-FAU-AvH/oc_rl_lqr We motivate the study of interconnections and relevant results at the interface between Control [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>PhD Thesis defense by A. Alvarez-Lopez</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-by-a-alvarez-lopez/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 29 Jun 2026 17:33:28 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33046</guid>

					<description><![CDATA[Next Wednesday July 1, 2026 our team member Antonio Álvarez-López will present his PhD Thesis on: &#8220;Deep Learning with Controlled Flows: Expressivity, Generalization and Generation&#8221; Advisors: • Prof. Enrique Zuazua, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) • Prof. Rafael Orive, Autonomous University of Madrid (UAM) Abstract. This thesis studies deep learning from the perspective of [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<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>Tue, 16 Jun 2026 11:13:22 +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>
		<category><![CDATA[News]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32265</guid>

					<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) #MLPDES26 Book of Abstracts *Recordings available at: https://mod.fau.eu/mlpdes26/ This Spring, our FAU MoD, Research Center for Mathematics of Data is [&#8230;]]]></description>
		
		
		
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		<item>
		<title>FAU MoD Lecture: Reverse typography and the theory of shape: Can old books be brought back to life?</title>
		<link>https://dcn.nat.fau.eu/fau-mod-lecture-reverse-typography-and-the-theory-of-shape-can-old-books-be-brought-back-to-life/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 15 Jun 2026 03:13:17 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32915</guid>

					<description><![CDATA[Date: Fri. June 19, 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: Reverse typography and the theory of shape: Can old books be brought back to life? Speaker: Prof. Dr. Jean‑Michel Morel Affiliation: Division of Industrial Data Science. School of [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Sums-of-squares Polyconvexity</title>
		<link>https://dcn.nat.fau.eu/sums-of-squares-polyconvexity/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 11 Jun 2026 11:13:50 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Ajay Murali]]></category>
		<category><![CDATA[Math Giovanni Fantuzzi]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33013</guid>

					<description><![CDATA[Sums-of-squares Polyconvexity Find this post at FAU DCN-AvH Math &#038; Research posts. &#160; 1 Introduction Polyconvex functions are functions of a matrix variable characterized by a convex dependence on the minors of the matrix. This generalization of convexity is fundamental in the Calculus of Variations, where it provides a sufficient condition for the well-posedness of [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>EZ 65: Control, PDEs and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/ez65-control-pdes-and-ml/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 02 Jun 2026 20:13:09 +0000</pubDate>
				<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Events]]></category>
		<category><![CDATA[News]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32375</guid>

					<description><![CDATA[Date: Mon.- Thu. June 08 &#8211; 11, 2026 Event: A conference in honour of Enrique Zuazua on the occasion of his 65th birthday Title: EZ 65: Control, PDEs and Machine Learning We are pleased to announce the conference &#8220;Control, PDEs and Machine Learning&#8221;, to be held at the University of Deusto in Bilbao from 8 [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Synthetic profile generation for energy system planning using Artificial Intelligence</title>
		<link>https://dcn.nat.fau.eu/synthetic-profile-generation-for-energy-system-planning-using-artificial-intelligence/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 19:58:04 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32933</guid>

					<description><![CDATA[Master Thesis: &#8220;Synthetic profile generation for energy system planning using Artificial Intelligence&#8221; Author: Aniruddha Maiti Supervisors: Prof. Enrique Zuazua, Zhengping Ji Date: June, 2026 Rapid urbanization and population growth have led to a significant increase in energy consumption, especially in densely populated metropolitan areas. Simultaneously, the European Union and Germany have set ambitious goals to [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Robust Creep Control Algorithm with motion shaping and tracking for passenger vehicles</title>
		<link>https://dcn.nat.fau.eu/robust-creep-control-algorithm-with-motion-shaping-and-tracking-for-passenger-vehicles/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 03:13:51 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32990</guid>

					<description><![CDATA[Master Thesis: &#8220;Robust Creep Control Algorithm with motion shaping and tracking for passenger vehicles&#8221; Author: Dante Amor Supervisors: Prof. Enrique Zuazua, Zhengping Ji Date: May, 2026 This thesis aims to provide a new architecture that provides torque commands to control the vehicle, not only reaching a target velocity, but by shaping the full trajectory of [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Activation Saturation and Jacobian Attenuation in Neural Ordinary Differential Equations</title>
		<link>https://dcn.nat.fau.eu/activation-saturation-and-jacobian-attenuation-in-neural-ordinary-differential-equations/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Sun, 31 May 2026 18:42:03 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Nikos Matzakos]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32951</guid>

					<description><![CDATA[Neural Ordinary Differential Equations (Neural ODEs) offer a principled way to model continuous dynamical systems with neural networks [1]. The hidden state evolves according to where is a multilayer perceptron with trained weights . Once training is finished, is fixed and will not change. The network no longer adapts&#8212;it defines a static vector field on [&#8230;]]]></description>
		
		
		
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