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Panda is a foundation model for forecasting chaotic dynamical systems with strong out-of-domain generalization, introduced by researchers from UT Austin. Trained on 20,000 algorithmically discovered chaotic ODEs, the model achieves zero-shot forecasting of unseen ODEs, real-world experimental data, and surprisingly, high-dimensional PDEs.