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arXiv 2409.08768math.DScs.LGmath.DG
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测度论视角下的时间延迟嵌入

Measure-Theoretic Time-Delay Embedding

Center for Applied Mathematics(应用数学中心);Cornell University(康奈尔大学);College of Information Sciences and Technology(信息科学与技术学院);Pennsylvania State University(宾夕法尼亚州立大学);Department of Mathematics(数学系)

Jonah Botvinick-Greenhouse, Maria Oprea, Romit Maulik, Yunan Yang

AI总结:

本文提出测度论时间延迟嵌入理论,将Takens嵌入推广到概率测度空间,并结合最优传输开发出对稀疏和噪声数据具有鲁棒性的计算程序,在Lorenz-63、NOAA海温及ERA5风场重建中验证了有效性。

摘要:

著名的Takens嵌入定理为从部分观测中重构动力系统的完整状态提供了理论基础。然而,经典定理假设底层系统是确定性的,并且观测是无噪声的,这限制了其在现实场景中的适用性。受这些局限性的启发,我们提出了一种测度论推广,采用动力学的欧拉描述,并将嵌入重新表述为概率测度空间之间的前推映射。我们的数学结果利用了最优传输领域的最新进展。基于所提出的测度论时间延迟嵌入理论,我们开发了一种计算程序,旨在从时间滞后的部分观测中重构动力系统的完整状态,并针对稀疏和噪声数据进行了鲁棒性设计。我们在多个数值示例上评估了基于测度的方法,从经典的Lorenz-63系统到现实世界应用,如NOAA海表温度重建和ERA5风场重建。

英文摘要:

The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that observations are noise-free, limiting its applicability in real-world scenarios. Motivated by these limitations, we formulate a measure-theoretic generalization that adopts an Eulerian description of the dynamics and recasts the embedding as a pushforward map between spaces of probability measures. Our mathematical results leverage recent advances in optimal transport. Building on the proposed measure-theoretic time-delay embedding theory, we develop a computational procedure that aims to reconstruct the full state of a dynamical system from time-lagged partial observations, engineered with robustness to handle sparse and noisy data. We evaluate our measure-based approach across several numerical examples, ranging from the classic Lorenz-63 system to real-world applications such as NOAA sea surface temperature reconstruction and ERA5 wind field reconstruction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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