AI RESEARCH
Stable Long-Horizon PDE Forecasting via Latent Structured Spectral Propagators
arXiv CS.LG
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ArXi:2605.10154v1 Announce Type: new Long-horizon forecasting of time-dependent partial differential equations (PDEs) is critical for characterizing the sustained evolution of physical systems. While neural operators have emerged as efficient surrogates, they typically learn implicit finite-time transitions from discrete observations. When deployed autoregressively, such propagators often suffer from rapid error accumulation and dynamic drift.