延迟物理系统的驱动因素与动力学的可辨识性保证
Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
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中文总结 AI 辅助
针对延迟物理系统，提出一种基于理论的方法，证明在宽松假设下随机延迟微分方程的结构驱动因素与漂移项可辨识，并在两个基准上优于现有方法。
中文摘要 AI 辅助
已有多种方法被提出，包括物理信息神经网络（强大但无法保证动力学的可辨识性）、符号回归（需要一组预计算的操作）以及因果发现（更具原则性但通常依赖于物理系统可能违反的强假设）。在本工作中，我们开发了一种基于理论的方法，并证明在一组宽松假设下，随机延迟微分方程的结构性驱动因素和漂移项是可辨识的。我们的方法在驱动因素可辨识性基准上优于其他方法，并在第二个评估学习动力学物理一致性的基准上表现更优。
英文摘要
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions that physical systems may violate. In this work, we develop a theory-grounded method and prove that under a set of permissive assumptions, the structural drivers and drift of stochastic delayed differential equations are identifiable. Our method outperforms others on a benchmark for driver identifiability, and on a second benchmark to evaluate physical consistency of the learned dynamics.
发表机构
- McGill University（麦吉尔大学）
- Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。
