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arXiv · 2606.22440

Data-driven geometric phase in biological locomotion

Pyae Hein Htet·Kenta Ishimoto
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Abstract

Geometric phase quantifies net locomotion in dissipative media via gauge theory, but linking this theoretical quantity to noisy, sparse, and weakly periodic biological shape data is challenging. We develop a theory-guided, data-driven Koopman autoencoder to recover the limit cycle embedded in imperfect cyclic data and extract shape gaits and geometric phase from sperm and nematode data. We introduce a geometric phase sensitivity function that quantifies responses to shape perturbations and reveals mechanical information using only gauge-theoretic structure, without assuming mechanical laws.

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Pyae Hein Htet, Kenta Ishimoto. 2026-06-21. Data-driven geometric phase in biological locomotion. https://arxiv.org/abs/2606.22440

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