Abstract: To address the problem that existing neural-network-based aerodynamic parameter identification methods can only obtain the overall aerodynamic coefficients but fail to separate their internal constituent components, an identification framework based on the Aerodynamic Incremental Neural Network (Aero-INN) is proposed. The framework is capable of separating the constituent components of aerodynamic coefficients without requiring a priori knowledge of the aerodynamic model structure, thereby achieving physically interpretable neural-network-based identification. The framework is developed based on the concept of aerodynamic incremental modeling. Instead of directly performing end-to-end modeling of the total aerodynamic coefficients, neural networks are employed to replace the aerodynamic derivative parameters in conventional models, such that the aerodynamic forces are expressed as the sum of a baseline aerodynamic subnetwork and several increment subnetworks with clear physical meanings. By embedding incremental physical constraints into the network architecture, the model outputs aerodynamic increments that conform to physical intuition. On this basis, a two-step identification strategy of “component extraction followed by white-box reconstruction” is proposed, in which the extracted increment subnetworks are converted into explicit interpolation tables and analytical expressions via regular grid sampling and symbolic regression. Validation is conducted using both three-axis excitation flight simulation data and near-stall real flight data. The results demonstrate that the proposed method ensures prediction accuracy of the total aerodynamic coefficients while enabling interpretable decomposition of internal aerodynamic components. By reducing the high-dimensional white-box modeling problem to white-box modeling in several low-dimensional subspaces, the difficulty of white-box reconstruction is significantly lowered, providing a new technical approach for improving the interpretability of neural-network-based nonlinear identification and for correcting the ground-to-flight correlation of aerodynamic databases in practical engineering applications.

Key words: aerodynamic parameter identification, incremental neural network, aerodynamic database, quasi-steady stall, ground-flight correlation correction, neural ordinary differential equation