ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.
Advances in Neural Information Processing Systems , volume=
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Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核