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GUM is widespread across momentum, adaptive, and curvature-based optimizers, with conflict rates reaching up to 86.3%.
Experimental results show that MuST-Flow achieves the lowest mean absolute error, highest structural similarity, and lowest speed and velocity-direction errors among both generic spatiotemporal prediction baselines and flow-oriented neural-operator baselines, while maintaining competitive divergence and Navier–Stokes r...
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