Many complex natural and cultural phenomena are well modelled by systems of
simple interactions between particles. A number of architectures have been
developed to articulate this kind of structure, both implicitly and explicitly.
We consider an unsupervised explicit model, the NRI model, and make a series of
representational adaptations and physically motivated changes. Most notably we
factorise the inferred latent interaction graph into a multiplex graph,
allowing each layer to encode for a different interaction-type. This fNRI model
is smaller in size and significantly outperforms the original in both edge and
trajectory prediction, establishing a new state-of-the-art. We also present a
simplified variant of our model, which demonstrates the NRI's formulation as a
variational auto-encoder is not necessary for good performance, and make an
adaptation to the NRI's training routine, significantly improving its ability
to model complex physical dynamical systems.

简述：通过在 NRI 模型中引入一种新的因子化方法，即将推断的潜在交互图分解为一种多层结构，使 fNRI 模型在边缘预测和轨迹预测方面表现明显优于原模型，同时也提出了模型的简化版，并对训练例程进行调整以大大改善其对复杂物理动态系统的建模能力。