We present the Neural Physics Engine (NPE), a framework for learning
simulators of intuitive physics that naturally generalize across variable
object count and different scene configurations. We propose a factorization of
a physical scene into composable object-based representations and a neural
network architecture whose compositional structure factorizes object dynamics
into pairwise interactions. Like a symbolic physics engine, the NPE is endowed
with generic notions of objects and their interactions; realized as a neural
network, it can be trained via stochastic gradient descent to adapt to specific
object properties and dynamics of different worlds. We evaluate the efficacy of
our approach on simple rigid body dynamics in two-dimensional worlds. By
comparing to less structured architectures, we show that the NPE's
compositional representation of the structure in physical interactions improves
its ability to predict movement, generalize across variable object count and
different scene configurations, and infer latent properties of objects such as
mass.

本文提出了神经物理引擎（NPE），它是一个学习具有直觉物理的模拟器的框架，并自然地推广到可变对象计数和不同场景配置。作者将物理场景分解为可组合的基于对象的表示，并使用神经网络架构对对象动态进行分解。通过与非结构化架构进行比较，作者表明，NPE 对物理交互中的结构表示改善了其预测运动能力，能够在不同场景中进行推广，并推断出物体的潜在属性。