Autoregressive One-Step Generative Modeling for Dynamical System Forecasting
Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories. Neural operators provide inexpensive autoregressive forecasts but can drift in turbulent regimes, whereas rolling diffusion and latent generative surrogates can represent stochastic transitions at the cost of multi-step denoising, noise-schedule design, or auxiliary compression models. We propose MeanFlow Long-term Invariant Spatiotemporal Consistency Autoregressive Models (MeLISA), a latent-free autoregressive generative surrogate built on pixel-space MeanFlow. MeLISA defines a bloc...
| Authors | Tianyue Yang, Xiao Xue |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 27 July 2026 |
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