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Stable by Construction: Variational Latent Markov Operators for Long-Horizon PDE Prediction
Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can induce distribution shift and accumulate under recursive deployment. We develop a variational approach to this problem by
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