用数据重构鸟类呼吸系统的隐藏肌肉力，揭示压力信号未显示的振荡机制。
Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks
- 基于柯尔莫哥洛夫-阿诺德网络，从气囊压力推断隐藏的肌肉驱动
- 发现呼吸周期内存在双相激活模式，与肌电图验证一致
- 可揭示部分观测系统中的隐藏物理机制，适合生物力学与动力系统研究者
从部分观测中推断驱动动力系统的力量是物理学中的基本挑战，尤其当不同机制产生相似可观测动态时。本文展示仅通过气囊压力测量即可重建鸟类呼吸动力学背后的有效肌肉力。采用基于柯尔莫哥洛夫-阿诺德网络的可解释学习框架，直接从数据中推导系统控制方程，揭示了压力信号本身未显现出的非平凡力结构，该结构暗示一种松弛型振荡。重构的动力学预测每个呼吸周期内存在两相激活模式，通过呼气肌的肌电图记录独立验证。结果表明，数据驱动的动力学重构可揭示隐藏的物理结构，并获取未观测到的驱动变量，为推断部分观测动力系统中的潜在力提供通用路径。
原文摘要 · Abstract (English)
Inferring the forces that drive a dynamical system from partial observations is a fundamental challenge across physics, particularly when distinct underlying mechanisms produce similar observable dynamics. Here we show that the effective muscular forcing underlying avian respiratory dynamics can be reconstructed from measurements of air-sac pressure alone. Using an interpretable learning framework based on Kolmogorov-Arnold networks, we infer the governing equations of the system directly from data and uncover a nontrivial structure in the underlying forcing that is not apparent from the pressure signal, which instead suggests a relaxation-like oscillation. The reconstructed dynamics predict a two-phase activation pattern within each respiratory cycle, which we independently validate through electromyographic recordings of expiratory muscles. These results demonstrate that data-driven reconstruction of dynamical laws can reveal hidden physical structure and provide access to unobserved driving variables, establishing a general route to infer latent forces in partially observed dynamical systems.
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