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. However%2C most existing studies focus only on multi-scale%2C multi-physics systems within a single spatial region%2C neglecting the case with multiple interconnected sub-regions%2C such as gas and thermal systems. To address this%2C this paper proposes a Physics-Informed Partitioned Coupled Neural Operator (PCNO) to enhance the simulation performance of such networks. Compared to the existing Fourier Neural Operator (FNO)%2C this method designs a joint convolution operator within the Fourier layer%2C enabling global integration capturing all sub-regions. Additionally%2C grid alignment layers are introduced outside the Fourier layer to help the joint convolution operator accurately learn the coupling relationship between sub-regions in the frequency domain. Experiments on gas networks demonstrate that the proposed operator not only accurately simulates complex systems but also shows good generalization and low model complexity.?quality=80&w=800)
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