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convolutional neural networks by exploring transformers, implicit neural representations (INRs), and hybrid architectures that integrate physical priors such as periodicity, symmetry, and long-range correlations
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). The intention ius also to explore hybrid models that combine the local sensitivity of CNNs with the global modeling capabilities of these emerging architectures. A central goal is to integrate physical priors
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