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新方法利用辛几何实现汉密尔顿系统中的数据高效控制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的数据驱动方法，通过利用辛几何和内在递归来实现汉密尔顿系统中的目标可达性。该方法旨在通过利用物理定律，特别是汉密尔顿动力学中嵌入的结构，来减少非线性控制的数据需求。所提出的技术结合了递归特性和链式策略，链式策略由演示中的局部认证轨迹段组成，以实现目标可达性。作者提供了可达性的条件，并证明数据需求取决于几何和递归特性，而不是状态维度。 AI

排序理由 该集群包含一篇详细介绍控制系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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新方法利用辛几何实现汉密尔顿系统中的数据高效控制

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该集群包含一篇详细介绍控制系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zhuo Ouyang, Jixian Liu, Enrique Mallada ·

    Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems

    arXiv:2604.17213v2 Announce Type: replace-cross Abstract: Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity, which underpins low sample-complexity gu…

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