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Tianjin University
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IA-NNP improves convergence in complex CZM simulations while preserving the original model's integrity, outperforming traditional methods.
ST-CND reveals that traditional spatial indicators can miss critical tipping points, offering a more accurate and interpretable approach to early warning in complex ecosystems.
Neural operators can stably and accurately correct the structured truncation errors of classical numerical solvers for dispersive PDEs, even with rough data.
COBALT unlocks efficient structural design optimization by treating the design space as a discrete anchored graph, avoiding the pitfalls of continuous relaxation and rounding-off that plague existing methods.
