DAFL: Disturbance-aware Field Learning for Robust CFD Warm-Starting

Aerodynamic design optimization necessitates repeated analyses across diverse geometries and flow conditions, each requiring costly computational fluid dynamics (CFD) simulations. Learning-based warm-starting offers a promising way to reduce solver iterations by predicting initial states closer to convergence. However, existing methods can fail on complex three-dimensional aerodynamic flows, where spatially heterogeneous disturbances and highly imbalanced flow variable distributions lead to inaccurate or even invalid predictions that undermine solver convergence. We propose Disturbance-aware Field Learning (DAFL), a framework for robust CFD warm-starting. DAFL adaptively partitions the flow field according to disturbance characteristics and performs disturbance-aware sampling and regression, enabling accurate modeling of critical disturbances. Geometry and flow conditions are injected throughout the backbone to capture case-dependent variations, while logarithmic wall distance improves the representation of steep near-wall velocity gradients. For flow variables with highly imbalanced distributions, DAFL performs regression in log space and maps predictions back through an exponential transformation, improving optimization while preserving physically valid initial states. Experiments on complex three-dimensional aircraft data demonstrate that DAFL consistently outperforms existing approaches in flow field prediction. More importantly, DAFL enables robust CFD warm-starting, substantial solver acceleration, and strong generalization across diverse geometries and flow conditions.