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Found 4 papers, 4 papers with code
Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
Our model employs a novel pairwise attention (PA) mechanism to refine the trajectory representations and a graph transformer to extract heterogeneous interaction weights for each pair of agents.
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment?
Discovering conservation laws from trajectories via machine learning
Invariants and conservation laws convey critical information about the underlying dynamics of a system, yet it is generally infeasible to find them from large-scale data without any prior knowledge or human insight.
Deep learning reveals hidden interactions in complex systems
Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists.
