English(EN) Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations

新框架CIPHER学习端口哈密顿系统中的潜变量动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了CIPHER，一个用于从部分观测中学习端口哈密顿系统潜变量动力学的新框架。这种两阶段方法使用对比学习首先识别预测状态表示，然后学习系统的动力学。CIPHER在各种数据集上展示了与现有方法相比具有竞争力或更优越的性能，显示出对复杂系统的鲁棒性和改进的长期预测能力。 AI

影响这项研究推进了从部分数据中学习复杂系统动力学的方法，有可能改善科学和工程应用中的预测和控制。

排序理由该集群包含一篇详细介绍学习系统动力学新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Peilun Li, Kaiyuan Tan, Daniel Moyer, Thomas Beckers · 2026-10-05 04:00

识别后实现：基于部分观测的潜在端口哈密顿动力学的对比学习

arXiv:2605.16682v2 Announce Type: replace Abstract: Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial and high-dimensional observations. In such settings, representation learning …