arXiv cs.AI· Yulong Yang, Jiayin Liu, Vineet Bansal, Christine Allen-Blanchette·· 6 天前AI 评分22
SPS-GAN：从轨迹与视频中学习哈密顿系统的相空间结构
Discovering Phase Space Structure in Learned Hamiltonian Systems
AI 导读
Structured Phase Space GAN（SPS-GAN）是一种条件生成模型，将 port-Hamiltonian 主干与对抗式条件生成结合，可在单一架构中表示保守、耗散与受迫系统，并从笛卡尔轨迹和视频中学习结构化动力学。
正文 · 原文
Abstract:Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and deterministic prediction from a prescribed initial condition. We introduce Structured Phase Space GAN (SPS-GAN), a conditional generative model that learns structured dynamics from both Cartesian trajectories and video. SPS-GAN combines a port-Hamiltonian backbone with adversarial conditional generation, allowing conservative, dissipative, and forced systems to be represented in a single architecture. To infer latent mechanical structure from raw observations, we use a cyclic-coordinate loss that recovers both the system degrees of freedom and canonical coordinates without access to the true phase space. Experiments on simulated and real-world systems show that SPS-GAN improves trajectory accuracy over supervised dynamics baselines and video consistency over generative baselines, while consistently recovering the correct latent mechanical coordinates across observation modalities and dynamical regimes.
| Comments: | Accept to The 40th Annual Conference on Neural Information Processing Systems (NeurIPS 2026). Camera-ready to come |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2509.23003 [cs.LG] |
| (or arXiv:2509.23003v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2509.23003 arXiv-issued DOI via DataCite |
Submission history
From: Yulong Yang [view email]
[v1]
Fri, 26 Sep 2025 23:46:55 UTC (7,705 KB)
[v2]
Wed, 14 Jan 2026 17:15:25 UTC (8,482 KB)
[v3]
Thu, 8 Oct 2026 20:45:56 UTC (12,455 KB)
来源：arXiv cs.AI · arxiv.org