A condition-driven globally aware point cloud network for rapid surface field prediction of cross-domain flight vehicles

Rapid prediction of aerodynamic and aerothermal surface fields is critical for the design and real-time monitoring of cross-domain flight vehicles, yet conventional computational fluid dynamics (CFD) remains prohibitively expensive. Here, a condition-driven globally aware point cloud network (CD-GPN) is presented, achieving millisecond-scale, near-CFD-accuracy predictions of surface pressure and heat flux. Based on PointNeXt, CD-GPN integrates three core components: geometrically enhanced inverted residual modules for local geometric perception; a condition feature modulation mechanism via feature-wise linear modulation to inject flight conditions into deep feature streams; and a global interaction module on condition-modulated latent modes to capture long-range flow-field correlations. Extensively tested across Mach 5–15, altitudes of 20–40 km, and angles of attack of 0°–10°, CD-GPN outperforms mainstream baselines. Specifically, it yields a mean normalized root mean square error of 1.77% for single-configuration pressure prediction, and 1.03% and 2.07% for multi-configuration pressure and heat flux predictions, respectively. Furthermore, it demonstrates good extrapolation robustness to unseen flight conditions and novel geometries, and transfer fine-tuning with only a small number of target-configuration samples can further reduce the cross-topology blind-prediction error. Meanwhile, in terms of efficiency, it reduces the single evaluation time from 45 min (CFD) to 134 ms. These results establish CD-GPN as an efficient, physics-aware surrogate bridging offline simulation and online monitoring for next-generation aerospace vehicle design.