Graph Mamba Operator: New Machine Learning Model for Simulating Interacting Particle Systems
Researchers have developed Graph Mamba Operator (GraMO), a new machine learning model that combines state-space models with graph-based learning to simulate complex systems of interacting particles. The model addresses limitations in existing approaches by coupling spatial interactions and temporal dynamics in a single framework rather than treating them separately. The advance could improve long-horizon predictions in physics simulations, motion capture, and robotics applications.
Graph Mamba Operator (GraMO) is a latent-space simulator designed to model interacting dynamical systems by capturing both spatial interactions and long-range temporal dependencies simultaneously. Traditional graph neural networks rely on autoregressive rollouts and separate spatial and temporal processing, which leads to error accumulation over extended prediction horizons and limits their ability to capture multi-hop dependencies and global structure. GraMO integrates state-space models with graph-based interaction learning, coupling these updates within a single recurrence where the update is linear in latent state with input-dependent coefficients that adapt across different regimes. The researchers evaluated GraMO on N-body systems, motion capture, and robotics datasets, reporting the lowest prediction error across benchmarks and the largest improvements in long-horizon prediction compared to existing methods. This work represents a methodological advance in neural network architecture design for physics-informed machine learning.
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- arXiv cs.LGCenter
Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
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