English(EN) Graph-Recurrent Learning of Emergent Collective Dynamics

图循环神经网络学习涌现集体动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种新颖的图循环神经网络（GRNN）框架，旨在直接从观测到的轨迹中学习涌现集体动力学。该GRNN集成了门控循环记忆与图消息传递，使其能够在不知道底层微观运动方程的情况下捕捉空间相互作用和时间记忆。该框架成功应用于Holstein模型，在畴粗化过程中准确地再现了时间和空间相关性，为描述复杂动力学提供了一种数据驱动的方法。 AI

影响为具有未解决微观细节的复杂物理系统提供了一种新的数据驱动建模方法。

排序理由详细介绍了一种新的机器学习模型及其应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Ali Rayat, Acacia Lee, Gia-Wei Chern · 2026-10-23 04:00

Graph-Recurrent Learning of Emergent Collective Dynamics

arXiv:2610.31350v1 Announce Type: cross Abstract: Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description. We introduce a graph-recurrent neural network (GRNN) f…

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Graph-Recurrent Learning of Emergent Collective Dynamics

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