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Kolmogorov-Arnold networks infer hidden biological forces from pressure data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method using Kolmogorov-Arnold networks to infer hidden forces driving biological systems from limited observational data. This approach was successfully applied to reconstruct the muscular forcing behind avian respiratory dynamics using only air-sac pressure measurements. The findings reveal a complex, two-phase activation pattern in expiratory muscles, validating the technique's ability to uncover latent physical structures and driving variables in partially observed dynamical systems. AI

IMPACT This research demonstrates a new data-driven method for inferring underlying physical laws and unobserved forces in complex systems, potentially applicable to various scientific domains.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Julian Szereszewski, Facundo Fainstein, Leandro E. Fernandez, Gabriel B. Mindlin ·

    Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks

    arXiv:2606.08479v1 Announce Type: new Abstract: 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 effe…

  2. arXiv cs.LG TIER_1 English(EN) · Gabriel B. Mindlin ·

    Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks

    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 respirat…

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