TORNADO lets PDE ensemble forecasts vary their noise by state, cutting calibration error roughly 2x to over 5x versus representative baselines at five network evaluations
Probabilistic PDE surrogates often turn overconfident in few-step inference, producing predictive spreads narrower than the real uncertainty. TORNADO recasts next-step prediction as stochastic transport between the variational-autoencoder posteriors of consecutive frames, and lets a neural network set the diffusion amplitude from the latent state instead of a fixed global noise schedule. On four synthetic PDE datasets, at a fixed budget of five network evaluations, it reaches the lowest calibration error, roughly 2x to over 5x lower than representative baselines, while keeping comparable predictive accuracy.