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		<title>Industrial &#038; Social Transference</title>
		<link>https://dcn.nat.fau.eu/transference/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:29:47 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
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		<title>CoDeFeL • GitHub</title>
		<link>https://dcn.nat.fau.eu/codefel-github/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:22:49 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
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					<description><![CDATA[Where mathematics becomes executable innovation. Explore the open repository for the project algorithms, software environment, and reproducible computational research and tools connecting at the interface of Control and Machine Learning. From robust deep-learning methods and federated learning to hybrid ResNet/PDE models. Designed for reproducible research and real-world impact, CoDeFeL equips researchers and practitioners to address [&#8230;]]]></description>
		
		
		
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		<title>CoDeFeL • WP4</title>
		<link>https://dcn.nat.fau.eu/codefel-wp4/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:00:03 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL WP]]></category>
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					<description><![CDATA[WP4 &#8211; Modelling through Control and Machine Learning Control theory provides a principled framework for regulating dynamical systems, while Machine Learning enables complex patterns and predictive models to be extracted from data. By integrating these two fields, we develop efficient, robust, and adaptive methodologies that combine data-driven learning with feedback mechanisms. This synergy provides a [&#8230;]]]></description>
		
		
		
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		<title>CoDeFeL • WP3</title>
		<link>https://dcn.nat.fau.eu/codefel-wp3/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 19:59:56 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL WP]]></category>
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					<description><![CDATA[WP3 &#8211; Federated Learning Federated Learning (FL) is a decentralized Machine Learning paradigm that enables multiple devices or agents to collaboratively train models without directly sharing their local data. This approach is particularly relevant in privacy-sensitive domains such as healthcare and finance, where data sharing is often constrained. Despite its rapid development, a substantial gap [&#8230;]]]></description>
		
		
		
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		<title>CoDeFeL • WP2</title>
		<link>https://dcn.nat.fau.eu/codefel-wp2/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 19:59:51 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
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					<description><![CDATA[WP2 &#8211; Complexity of ResNet Dynamics Residual Neural Networks (ResNets) leverage shortcut connections to facilitate gradient propagation, enabling faster convergence and enhanced robustness. Their residual structure establishes a natural connection with time-discrete dynamical systems, revealing distinctive properties that can be studied and exploited through control-theoretic tools. Building on simultaneous control theory and universal approximation principles, [&#8230;]]]></description>
		
		
		
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		<title>CoDeFeL • WP1</title>
		<link>https://dcn.nat.fau.eu/codefel-wp1/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 19:59:49 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL WP]]></category>
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					<description><![CDATA[WP1 &#8211; Asymptotics and Turnpike for Deep Neural Networks Turnpike theory, originally an economic concept, describes how optimal trajectories remain close to a stable, efficient regime — the &#8220;turnpike&#8221; — for most of their evolution, departing from it primarily to accommodate initial and terminal conditions. In Machine Learning, this principle provides a powerful framework for [&#8230;]]]></description>
		
		
		
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		<title>NUS Colloquium Series: Machine Learning and Learning Systems by E. Zuazua</title>
		<link>https://dcn.nat.fau.eu/nus-machine-learning-and-learning-systems-ezuazua-02oct2026/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 29 Sep 2026 10:54:58 +0000</pubDate>
				<category><![CDATA[EZuazua]]></category>
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					<description><![CDATA[Event: NUS Colloquium Series, National University of Singapore Date: Fri. October 02, 2026 Seminar: Machine Learning and Learning Systems Speaker: Prof. Enrique Zuazua. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Abstract. Machine Learning is creating a new universe of systems that compute, learn, interact, and act. Understanding their behavior raises fundamental mathematical questions about representation, inference, dynamics, optimization, [&#8230;]]]></description>
		
		
		
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		<title>Springer Nature &#8211; Celebrating Enrique Zuazua at 65: Control, PDEs and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/springer-nature-celebrating-enrique-zuazua-at-65/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 21 Sep 2026 20:28:36 +0000</pubDate>
				<category><![CDATA[EZuazua]]></category>
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		<category><![CDATA[fau]]></category>
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					<description><![CDATA[Open for submissions Submission deadline: March 31, 2027 Event: Celebrating Enrique Zuazua at 65: Control, PDEs and Machine Learning Organizer: Springer Nature Link Multiple participating journals &#160; Bringing together contributions inspired by the breadth of his scientific work, this collection highlights recent advances in the areas that Professor Enrique Zuazua has helped shape, while reflecting [&#8230;]]]></description>
		
		
		
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		<title>FAU/JLU Workshop on Recent Trends in Applied Mathematics and Machine Learning 2026</title>
		<link>https://dcn.nat.fau.eu/workshop-trends-appliedmath-and-ml-2026/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 14 Sep 2026 10:28:43 +0000</pubDate>
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					<description><![CDATA[Date: Thu.- Sat. September 17 &#8211; 19, 2026 Event: FAU/JLU Workshop on Recent Trends in Applied Mathematics and Machine Learning 2026 Title: Recent Trends in Applied Mathematics and Machine Learning 2026 This September, Jilin University will host the Workshop on Recent Trends in Applied Mathematics and Machine Learning 2026, organized in collaboration with our FAU [&#8230;]]]></description>
		
		
		
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		<title>Mini Course: Computational PDEs and Scientific Machine Learning</title>
		<link>https://dcn.nat.fau.eu/mini-course-computational-pdes-and-scientific-machine-learning/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 08 Sep 2026 12:32:58 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Daniel Fernández M.]]></category>
		<category><![CDATA[Course]]></category>
		<category><![CDATA[Resources]]></category>
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					<description><![CDATA[Date: September 21, 23, 25, 28, 2026 Event: Mini Course Organizer: FAU and UFF, CAPES/DAAD PROBRAL. Mini-Course: Computational PDEs and Scientific Machine Learning Speaker: Daniel Fernández Martínez, PhD student at FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship. Friedrich-Alexander-Universität Erlangen-Nürnberg Resources • Course &#124; All resources • Course [&#8230;]]]></description>
		
		
		
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