Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems
Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation during long-term autoregressive rollouts. This behavior is partly tied to the nature of the underlying systems: chaotic spatiotemporal systems visit phase space unevenly, with dynamics dominated by recurrent, low-dimensional quiescent states and characterized by rare and dynamically complex regime transitions. Trained under a sample-wise uniform objective, standard neural surrogates allocate their finite capacity to the statistically more numerous low-dimensional quiescent states, systematically under-representin...
| Authors | Zhou Fang, Gianmarco Mengaldo |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 29 September 2026 |
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