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graphs, check the correctness of AI-generated structures, and even guide neural networks during inference. By combining techniques from grammatical inference, reinforcement learning, and efficient search
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trustworthiness of mathematical models and machine learning tools (e.g., neural networks) in a meaningful way, we need innovative, scalable methodologies that efficiently and accurately capture, represent, and
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neuromorphic circuits. We will also simulate high-efficiency spiking neural networks (SNN) and build neuromorphic sensory systems to validate performance and explore broad biomedical and other potential
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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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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas