# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 3 AI for UAV Safety
### UAV Physical Safety Design  
Physical safety design-related technologies are fundamental in UAV safety. Material design and flutter design are two key elements of physical safety design. The development and application of high-performance composite materials can reduce aircraft weight and increase structural strength and damage resistance, thereby improving overall structural efficiency. For complex configurations of low-altitude equipment, flutter analysis and control technologies can effectively prevent structural damage or flight accidents caused by aeroelastic problems during flight, ensuring flight safety. Traditional physical safety design models simplify nonlinear interactions and assume that mechanisms can be modeled using second-order linear ordinary differential equations. This approach may not fully capture all real-world influencing factors, reducing the model's accuracy and affecting UAV flight safety [94]. In terms of material design, ML methods have been employed to accelerate the manufacturing process of composite materials to improve material performance [95] and strength [96]. Moreover, to address the perennial issue of insufficient datasets, [PERSON] et al. proposed a transfer learning model based on a CGCNN pretrained with a low-precision PBE dataset and fine-tuned with a high-precision HSE06 dataset to predict the high-precision bandgap of crystal structures [97]. This approach overcomes the problems of low accuracy and overfitting caused by small datasets. In aircraft flutter prediction and suppression, DL models can identify flutter parameters in flight test signals, thereby facilitating improvements in analysis accuracy [94]. Additionally, RL techniques can be employed for active flutter suppression of flexible wings, enhancing flight stability [98]. In 2021, [PERSON] et al. applied neural networks to the active control of the nonlinear elastic response of aircraft, including flutter suppression, predictive control, and inverse model control [99]. These systems have been implemented to successfully suppress flutter in experiments, demonstrating the potential of neural networks in flutter suppression. In terms of transonic flutter management, Boeing [100] conducted Navier-Stokes simulations and limit cycle oscillation analysis. AI algorithms were applied to identify critical flutter points with an error of less than 5%, significantly outperforming traditional linear methods. AI algorithms have also been applied to predict aircraft structural stress and detect damage. For example, multilayer perceptron neural networks have been used to predict the stress field of wing rib structures, facilitating computational efficiency improvements [101]. Moreover, AI and acoustic emission optimization techniques have been integrated to detect impact damage in aircraft composite structures, enhancing the level of automation in damage detection [102]. However, there may be inconsistent data formats and incomplete records of experimental conditions in existing shared databases. For example, different institutions may use different testing standards or data storage formats, making data fusion difficult and limiting the effectiveness of training AI models. Moreover, given the ever-changing types of aircraft and flight conditions [103], existing models may lack detailed data on image resolution, sensor noise, control surface excitation signal amplitude variations [104], and different flight structural components [101], resulting in low adaptability and inaccurate control models over long-term operations. Moreover, neural networks often perform poorly in low-order modes and frequently exhibit oscillations, which can obscure the detection of flutter and result in inaccurate identification of flutter modes [105]. Additionally, the optimization of aircraft impact-resistant structures must simultaneously address constraints such as strength, weight, and cost, while traditional RL converges slowly in nonconvex spaces. Currently, LLMs have shown versatility across various applications in physical safety design.