Differential-Integral Neural Operator for Long-Term Turbulence Forecasting

arXiv:2509.21196v3 Announce Type: replace Abstract: Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applications ranging from climate modeling to aerospace engineering. Existing deep learning methods, particularly neural operators, often fail in long-term autoregressive predictions, suffering from catastrophic error accumulation and a loss of physical fidelity. This failure stems from their inability to simultaneously capture the distinct mathematical structures that govern turbulent dynamics: local, dissipati

Source: arXiv cs.LG — read the full report at the original publisher.

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