发表机构
Max Planck Institute for Software Systems; University of Birmingham（马克斯·普朗克软件系统研究所; 伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于Koopman理论的框架，利用系统轨迹学习线性潜在空间表示，通过谱性质提前预测亚稳态，主特征值可作为检测指标。
AI 中文摘要
亚稳态——系统在罕见扰动下突然转变之前被困在准稳定状态的现象——在物理系统中普遍存在。尽管亚稳态是一种广泛观察到的现象，但其识别和分析面临重大挑战。为了解决这些挑战，我们提出了一种利用Koopman理论分析亚稳态的新框架。我们使用有限的一组系统轨迹来学习动力学的表示，该表示定义了一个潜在空间，系统在其中线性演化，从而通过线性映射的谱性质实现对亚稳态行为的系统表征。实证评估表明，我们的方法能够显著早于亚稳态实际表现来预测其行为，即使在仿真时长的$10\%$时也能做到。此外，我们确定潜在空间中学习到的Koopman矩阵的主特征值可作为检测单服务器和多服务器配置中亚稳态的关键指标。
英文摘要
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, its identification and analysis present significant challenges. To address these challenges, we propose a novel framework for analyzing metastability using Koopman theory. We use a finite set of system trajectories to learn a representation of the dynamics that defines a latent space in which the system evolves linearly, thereby enabling a systematic characterization of metastable behavior through the spectral properties of the linear mapping. Empirical evaluations demonstrate that our approach is capable of anticipating metastable behavior significantly earlier than its actual manifestation, even with $10\%$ of the simulation duration. Moreover, we establish that the dominant eigenvalue of the learned Koopman matrix in the latent space serves as a critical indicator for detecting metastability across both single-server and multi-server configurations.
CommentsHSCC'26
