Differential-Integral Neural Operator for Long-Term Turbulence Forecasting

ArXi:2509.21196v3 Announce Type: replace 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.