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methodology that develops physics-informed graph neural networks (GNNs) for composite space structure analysis. The methodology will aim to leverage the inherent structure of composite materials, embedding
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: graph neural networks, natural language processing, algorithmic learning, fault-tolerance, blockchains, consensus, cryptocurrencies, digital money, central bank digital currency, decentralized finance
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learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural language processing, algorithmic learning, fault
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