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New method uses ML to find periodic orbits in dynamical systems

Researchers have developed a new Hessian-based method for numerically continuing periodic orbits in dynamical systems, utilizing Fourier series to parameterize orbits and automatic differentiation for Jacobian automation. This approach, which is integrator-free, efficiently identifies bifurcations and intersections of orbit families. The method was demonstrated by successfully continuing periodic oscillations of a double pendulum, revealing previously undocumented periodic orbits where both masses are never simultaneously at rest. AI

IMPACT This research demonstrates a novel application of machine learning techniques, specifically automatic differentiation, for advancing the study of complex dynamical systems.

RANK_REASON The cluster contains a single academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New method uses ML to find periodic orbits in dynamical systems

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The cluster contains a single academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Leo Yao, Ziming Liu, Max Tegmark ·

    Variational Continuation for Double Pendulum Periodic Orbits

    arXiv:2609.05337v1 Announce Type: new Abstract: We present a Hessian-based approach to numerically continue periodic orbits in dynamical systems. A loop (periodic orbit candidate) is parametrized as a Fourier series; a loss function is defined based on the deviation of the loop f…