KoopSTD: Faithful Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Original language	English
Title of host publication	The 42rd International Conference on Machine Learning (ICML’25)
Publication status	Published - 2025

Cite this

@inproceedings{a3b579ee72d54d82b22c0a950f2055c5,

title = "KoopSTD: Faithful Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling.",

author = "Shimin Zhang and Ziyuan Ye and Yinsong Yan and Zeyang Song and Yujie Wu and Jibin Wu",

year = "2025",

language = "English",

booktitle = "The 42rd International Conference on Machine Learning (ICML{\textquoteright}25)",

}

KoopSTD: Faithful Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling. / Zhang, Shimin; Ye, Ziyuan; Yan, Yinsong et al.
The 42rd International Conference on Machine Learning (ICML’25). 2025.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - KoopSTD: Faithful Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling.

AU - Zhang, Shimin

AU - Ye, Ziyuan

AU - Yan, Yinsong

AU - Song, Zeyang

AU - Wu, Yujie

AU - Wu, Jibin

PY - 2025

Y1 - 2025

M3 - Conference article published in proceeding or book

BT - The 42rd International Conference on Machine Learning (ICML’25)

ER -