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This paper proposes a novel deep learning-based approach for solving partial differential equations, which outperforms traditional numerical methods in accuracy and computational efficiency. The authors demonstrate the effectiveness of their method on a range of benchmark problems, highlighting its potential for widespread applications in science and engineering.