English(EN) Data-efficient Kernel Methods for Learning Hamiltonian Systems

新的核方法在数据有限的情况下改进了哈密顿系统模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了新的基于核的方法，使用轨迹数据来模拟哈密顿系统。这些方法包括一个两步法和一个一步法，旨在即使在数据有限的情况下也能准确预测系统行为并学习底层的哈密顿结构。与现有基线相比，所提出的框架在 Hénon-Heiles 系统和质量弹簧动力学等基准系统上表现出优越的性能，同时还提供了一个更通用的数值框架，适用于任意动力学系统。 AI

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arXiv stat.ML TIER_1 English(EN) · Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi, Peyman Tavallali, Houman Owhadi · 2026-09-04 04:00

用于学习哈密顿系统的低数据效率核方法

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…

本文如何被排名

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用于学习哈密顿系统的低数据效率核方法

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