Controlling Transient Amplification Improves Long-horizon Rollouts
Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when rolled out over long horizons. In this work, we identify transient amplification of perturbations around rollout trajectories as a structural mechanism driving rollout error. Using a linearization analysis we show that when the Jacobians along an autoregressive trajectory are non-normal and non-commuting, the model amplifies errors transiently, resulting in model rollout drift even when the overall system is asymptotically stable. Building on the analysis, we propose commutativity regularization: a combination of two penalties designed to r...
| Authors | Adeel Pervez, Francesco Locatello |
|---|---|
| Primary category | cs.LG |
| Published | 9 May 2026 |
| Last updated at source | 15 May 2026 |
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