arXiv:2604. 19465v3 Announce Type: replace-cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fundamental challenge across science and engineering.
By Chengyun Wang, Liwei Chen, Nils Thuerey
arXiv:2609.37083v1 Announce Type: cross
Abstract: We study the problem of recovering the governing ODE of a dynamical system from unstructured, high-dimensional observations such as images. Existing...
By Alessandro Trenta, Riccardo Massidda, Davide Bacciu, Sara Magliacane
arXiv:2608.22112v1 Announce Type: cross
Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approac...
By Nibodh Boddupalli, Jeff Moehlis
arXiv:2604. 24662v2 Announce Type: replace-cross Abstract: Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences.
By K. Michael Martini, Eslam Abdelaleem, Paarth Gulati, Ilya Nemenman
arXiv:2602. 04643v2 Announce Type: replace Abstract: Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing precursor dynamics.
By Yanan He, Yunshi Wen, Xin Wang, Tengfei Ma
arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.
By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng