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modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data. The development of these methods is motivated by a concrete and important application: inferring gas
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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external tools and memory components to execute complex reasoning and decision-making tasks. These agents are increasingly deployed in domains such as healthcare, finance, cy- bersecurity, and autonomous
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artificial intelligence (AI) sys- tems, integrating LLMs with external tools and memory components to execute complex reasoning and decision-making tasks. These agents are increasingly deployed in domains
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-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas dynamics in urban environments. Gas dynamics shape air
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components to execute complex reasoning and decision-making tasks. These agents are increasingly deployed in domains such as healthcare, finance, cybersecurity, and autonomous vehicles, where they interact