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New kernel methods improve Hamiltonian system simulation with limited data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new kernel-based methods for simulating Hamiltonian systems using trajectory data. These methods, including a two-step and a one-step approach, aim to accurately forecast system behavior and learn the underlying Hamiltonian structure, even with limited data. The proposed framework demonstrates superior performance on benchmark systems like the Hénon-Heiles system and mass-spring dynamics compared to existing baselines, while also offering a more general numerical framework applicable to arbitrary dynamical systems. AI

RANK_REASON The cluster contains a research paper detailing new methods for simulating dynamical systems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New kernel methods improve Hamiltonian system simulation with limited data

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The cluster contains a research paper detailing new methods for simulating dynamical systems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi, Peyman Tavallali, Houman Owhadi ·

    Data-efficient Kernel Methods for Learning Hamiltonian Systems

    arXiv:2509.17154v2 Announce Type: replace-cross Abstract: Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many scientific and engineering problems. In this work, we propose kernel-based meth…

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