With the widespread application of numericalsimulation in aeroengine, it is crucial to automatically and efficiently generate structured grids for rotating components. Therefore, a comprehensive structured grid autom...
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With the widespread application of numericalsimulation in aeroengine, it is crucial to automatically and efficiently generate structured grids for rotating components. Therefore, a comprehensive structured grid autom...
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Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, w...
Traditional text-based person ReID assumes that person descriptions from witnesses are complete and provided at once. However, in real-world scenarios, such descriptions are often partial or vague. To address this lim...
Recent advancements in Mamba have shown promising results in image restoration. These methods typically flatten 2D images into multiple distinct 1D sequences along rows and columns, process each sequence independently...
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In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural Networks(PINNs). Our approach leverage...
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Solar power is a vital energy source for stratospheric airships, and the layout of solar cells significantly influences both their performance and the airship's parameters. A numerical discretization method is emp...
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Advances in deep learning have enabled physics-informed neural networks to solve partial differential equations. numerical differentiation using the finite-difference (FD) method is efficient in physics-constrained de...
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