Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence.

The high computational cost of high-fidelity CFD simulations forms a major bottleneck in aerodynamic design. This paper introduces a multi-fidelity transfer learning framework to rapidly predict airfoil aerodynamics with high accuracy. Our approach involves pre-training a deep fully connected neural network on a large dataset of low-fidelity Euler simulations. The pre-trained model is then fine-tuned using a limited set of high-fidelity RANS data, enabling efficient knowledge transfer from low- to high-fidelity domains. A specialized logarithmic-exponential normalization method is developed to handle the scale differences between aerodynamic coefficients. The framework demonstrates exceptional performance: after fine-tuning with only 700 high-fidelity samples, the model accurately predicts pressure distributions (lowest RMSE = 0.053) and force coefficients (R² > 0.947 for lift and drag). This method successfully bridges the gap between computational efficiency and high accuracy, providing a powerful data-driven surrogate model that can significantly accelerate the aerodynamic design and optimization process.