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Graph-Recurrent Neural Network Learns Emergent Collective Dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel graph-recurrent neural network (GRNN) framework designed to learn emergent collective dynamics directly from observed trajectories. This GRNN integrates gated recurrent memory with graph message passing, enabling it to capture spatial interactions and temporal memory without requiring knowledge of the underlying microscopic equations of motion. The framework was successfully applied to the Holstein model, accurately reproducing temporal and spatial correlations during domain coarsening, offering a data-driven approach for describing complex dynamics. AI

IMPACT Provides a new data-driven method for modeling complex physical systems with unresolved microscopic details.

RANK_REASON Academic paper detailing a new machine learning model and its application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Graph-Recurrent Neural Network Learns Emergent Collective Dynamics

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Academic paper detailing a new machine learning model and its application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ali Rayat, Acacia Lee, Gia-Wei Chern ·

    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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