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English(EN) Learning Metastable Dynamics

新AI方法解决分数阶和亚稳态动力学问题

研究人员正在开发分析复杂系统动力学的新方法。一种方法侧重于从单轨迹中学习分数阶线性时不变系统,提出了一种解耦辨识问题的网格搜索估计器,并将误差界限缩放到 O(t^-1/2)。另一项研究利用 Koopman 理论分析亚稳态,即系统在转换前被困在准稳定状态的现象,方法是在潜在空间中学习动力学的线性表示。该框架可以预测亚稳态行为,并使用 Koopman 矩阵的主特征值作为关键指标。 AI

影响 这些方法有望促进跨领域复杂系统的科学理解和建模。

排序理由 两篇 arXiv 论文提出了分析复杂系统动力学的新研究方法。

在 arXiv cs.LG 阅读 →

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新AI方法解决分数阶和亚稳态动力学问题

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两篇 arXiv 论文提出了分析复杂系统动力学的新研究方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaole Zhang, Ziyi Zhang, Zehao Zhao, Stephen Tu, Guannan Qu, Yorie Nakahira, Paul Bogdan ·

    从单条轨迹中学习分数阶动力学

    arXiv:2609.18127v1 Announce Type: new Abstract: Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This paper studies system identification for discrete-tim…

  2. arXiv cs.LG TIER_1 English(EN) · Rupak Majumdar, Mahmoud Salamati, Nikhil Singh, Sadegh Soudjani ·

    学习亚稳态动力学

    arXiv:2609.14712v1 Announce Type: cross Abstract: Metastability---a phenomenon where systems remain trapped in quasi-stable states before abruptly transitioning under rare perturbations---is ubiquitous in physical systems. Although metastability is a widely observed phenomenon, i…