arXiv cs.AI· Shivanshu Tripathi, Maziar Raissi·· 13 小时前AI 评分31
Hybrid-time PINNs：面向混合动力系统的跳变感知物理信息神经网络
Hybrid-time PINNs: Discontinuity-Aware Physics-Informed Neural Networks for Learning Hybrid Dynamical Systems
AI 导读
研究者提出 hybrid-time PINNs（HT-PINNs），为混合时间域的每个连续流区间各分配一个网络，并与跳变时刻、未知参数联合训练，从而建模混合动力系统中的离散跳变。该方法通过枚举确定跳变次数与模态序列，在弹跳球案例中恢复了跳变次数与恢复系数；在 MuJoCo 机器人拆解仿真中，以 0.1% 和 1.7% 的小误差估计出静摩擦与动摩擦系数。
正文 · 原文
Abstract:Hybrid dynamical systems flow continuously but jump at discrete events. Physics-informed neural networks (PINNs) fit data while enforcing known dynamics, but a single network is smooth in time and cannot model these jumps. We show that the more closely a PINN follows a jump, the larger its physics loss must be. We propose hybrid-time PINNs (HT-PINNs), which assign one network to each flow interval of the hybrid time domain and train the networks jointly with the jump times and the unknown parameters. The number of jumps and the sequence of modes are found by enumeration. HT-PINNs recover the number of jumps and the restitution coefficient of a bouncing ball. In a MuJoCo simulation of robotic disassembly, where a robot pulls a press-fitted pin out of a housing through a spring, HT-PINNs find the number of jumps and the sequence of modes, and estimate the static and kinetic friction coefficients with small errors of 0.1% and 1.7%.
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33562 [eess.SY] |
| (or arXiv:2610.33562v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33562 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shivanshu Tripathi [view email]
[v1]
Sat, 24 Oct 2026 09:29:58 UTC (533 KB)
来源：arXiv cs.AI · arxiv.org