Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Flexible Neural Representation for Physics Prediction   Humans have a remarkable capacity to understand the physical dynamics of
objects in their environment, flexibly capturing complex structures and
interactions at multiple levels of detail. Inspired by this ability, we propose
a hierarchical particle-based object representation that covers a wide variety
of types of three-dimensional objects, including both arbitrary rigid
geometrical shapes and deformable materials. We then describe the Hierarchical
Relation Network (HRN), an end-to-end differentiable neural network based on
hierarchical graph convolution, that learns to predict physical dynamics in
this representation. Compared to other neural network baselines, the HRN
accurately handles complex collisions and nonrigid deformations, generating
plausible dynamics predictions at long time scales in novel settings, and
scaling to large scene configurations. These results demonstrate an
architecture with the potential to form the basis of next-generation physics
predictors for use in computer vision, robotics, and quantitative cognitive
science.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc