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Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes.
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Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
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Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
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