用物理先验增强高斯过程，实现任意飞行数据下的安全包线扩展。
Physics-informed Gaussian Processes for Safe Envelope Expansion
- 将气动模型作为先验嵌入高斯过程，提升预测精度。
- 无需预设测试点，准确预测T-38的短周期频率与阻尼特性。
- 适合飞行测试优化与高不确定性环境下的安全分析。
飞行测试分析通常依赖预先设定的测试点和过严容差，导致实验耗时耗力。为此，本文提出一种基于物理信息高斯过程的新方法，利用真实T-38飞机数据（与美国空军试飞员学校合作采集）估算气动参数，无需预设或重复测试点即可估计俯仰力矩系数，显著减少试验规模。该方法在高斯过程框架中引入气动模型作为先验，提升复杂飞行条件下预测准确性，并提供可靠的不确定性量化。关键贡献包括将物理先验融入概率模型，实现从任意飞行机动中精确计算；成功捕捉短周期模态等动态特征。所提框架可高效拓展至多种马赫数和动压条件，对短周期频率与阻尼的预测准确，具备可扩展性和通用性。
原文摘要 · Abstract (English)
Flight test analysis often requires predefined test points with arbitrarily tight tolerances, leading to extensive and resource-intensive experimental campaigns. To address this challenge, we propose a novel approach to flight test analysis using Gaussian processes (GPs) with physics-informed mean functions to estimate aerodynamic quantities from arbitrary flight test data, validated using real T-38 aircraft data collected in collaboration with the United States Air Force Test Pilot School. We demonstrate our method by estimating the pitching moment coefficient without requiring predefined or repeated flight test points, significantly reducing the need for extensive experimental campaigns. Our approach incorporates aerodynamic models as priors within the GP framework, enhancing predictive accuracy across diverse flight conditions and providing robust uncertainty quantification. Key contributions include the integration of physics-based priors in a probabilistic model, which allows for precise computation from arbitrary flight test maneuvers, and the demonstration of our method capturing relevant dynamic characteristics such as short-period mode behavior. The proposed framework offers a scalable and generalizable solution for efficient data-driven flight test analysis and is able to accurately predict the short period frequency and damping for the T-38 across several Mach and dynamic pressure profiles.
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