Despite significant analysis of bird flight, generative physics models for flight dynamics do not currently exist. Yet the underlying mechanisms responsible for various flight manoeuvres are important for understanding how agile flight can be accomplished. Even in a simple flight, multiple objectives are at play, complicating analysis of the overall flight mechanism. Using the data-driven method of dynamic mode decomposition (DMD) on motion capture recordings of hawks, we show that multiple behavioral states such as flapping, turning, landing, and gliding, can be modeled by simple and interpretable modal structures (i.e. the underlying wing-tail shape) which can be linearly combined to reproduce the experimental flight observations. Moreover, the DMD model can be used to extrapolate naturalistic flapping. Flight is highly individual, with differences in style across the hawks, but we find they share a common set of dynamic modes. The DMD model is a direct fit to data, unlike traditional models constructed from physics principles which can rarely be tested on real data and whose assumptions are typically invalid in real flight. The DMD approach gives a highly accurate reconstruction of the flight dynamics with only three parameters needed to characterize flapping, and a fourth to integrate turning manoeuvres. The DMD analysis further shows that the underlying mechanism of flight, much like simplest walking models, displays a parametric coupling between dominant modes suggesting efficiency for locomotion.

翻译：尽管对鸟类飞行进行了大量分析，但目前尚不存在用于飞行动力学的生成式物理模型。然而，理解各种飞行动作背后的基本机制对于认识敏捷飞行的实现方式至关重要。即使在简单飞行中，也涉及多个目标，这使得整体飞行机制的分析变得复杂。通过对鹰类运动捕捉记录应用动态模态分解（DMD）这一数据驱动方法，我们证明诸如扑翼、转向、着陆和滑翔等多种行为状态，均可通过简单且可解释的模态结构（即基础的翼尾形态）进行建模，这些模态可通过线性组合复现实验观测到的飞行数据。此外，DMD模型可用于外推自然状态的扑翼运动。飞行具有高度个体差异性，不同鹰类之间存在风格差异，但我们发现它们共享一组共同的动态模态。与传统基于物理原理构建的模型不同，DMD模型直接拟合数据——传统模型很少能在真实数据上进行验证，且其假设在真实飞行中通常不成立。DMD方法仅需三个参数表征扑翼运动，第四个参数整合转向动作，即可实现飞行动力学的高度精确重构。DMD分析进一步揭示：飞行的基础机制与最简单的步行模型相似，在主导模态间存在参数耦合现象，这暗示了运动效能优化的内在逻辑。