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Traditional finite element analysis methods require a fine mesh to achieve highly accurate stress fields, which is computationally expensive and time-consuming. This paper presents a novel neural network-based approach that learns the mapping between global displacement values on coarse global elements and local von Mises stress on fine local elements. By training the neural network on a dataset of paired global displacement and local stress fields, our method effectively predicts high-resolution stress distributions from coarse-grained inputs. This approach significantly reduces the computational burden associated with fine mesh finite element analysis, enabling real-time simulations and optimizations. 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