利用Kolmogorov-Arnold网络推断生物振荡器中的隐藏驱动力
Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks
AI总结：
提出利用Kolmogorov-Arnold网络从气压测量数据重建鸟类呼吸动力学方程，揭示隐藏的两相肌肉激活模式，并通过肌电图验证。
摘要：
从部分观测中推断驱动动力系统的力是物理学中的一个基本挑战，特别是当不同的潜在机制产生相似的观测动力学时。在这里，我们展示了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驱动力。使用基于Kolmogorov-Arnold网络的可解释学习框架，我们直接从数据中推断系统的控制方程，并揭示潜在驱动力中的非平凡结构，该结构从压力信号中并不明显，而压力信号反而暗示了一种类似松弛的振荡。重建的动力学预测每个呼吸周期内存在两相激活模式，我们通过呼气肌的肌电图记录独立验证了这一点。这些结果表明，数据驱动的动力学定律重建可以揭示隐藏的物理结构，并提供对未观测驱动变量的访问，从而建立了一种在部分观测动力系统中推断潜在力的通用途径。
英文摘要：
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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