GraphParticleSim

DGL Implementation of Interaction-Network paper.

This DGL example implements the GNN model proposed in the paper Interaction Network.

GraphParticleSim implementor

This example was implemented by Ericcsr during his Internship work at the AWS Shanghai AI Lab.

The graph dataset used in this example

This Example uses Datasets Generate By Physics N-Body Simulator adapted from This Repo

n_body: - n Particles/Nodes - Complete Bidirectional Graph - 10 trajectories should be generated - 1000 steps of simulation per trajectory

Dependency

• ffmpeg 4.3.8
• opencv-python 4.2.0

How to run example files

In the graphsim folder, run Please first run n_body_sim.py to generate some data

Using Ground Truth Velocity From Simulator Directly.

python n_body_sim.py

Generate Longer trajectory or more trajectories.

python n_body_sim.py --num_traj <num_traj> --steps <num_steps>

Please use train.py

python train.py --num_workers 15

Training with GPU

python train.py --gpu 0 --num_workers 15

Training with visualization: for valid visualization, it might take full 40000 epoch of training

python train.py --gpu 0 --num_workers 15 --visualize

One Step Loss Performance, Loss of test data after 40000 training epochs.

Models/Dataset	6 Body
Interaction Network in DGL	80(10)
Interaction Network in Tensorflow	60

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Notice that The datasets are generated directly from simulator to prevent using Tensorflow to handle the original dataset. The training is very unstable, the even if the minimum loss is achieved from time to time, there are chances that loss will suddenly increase,in both auther's model and our model. Since the original model hasn't been released, the implementation of this model refers to Tensorflow version implemented in: https://github.com/jsikyoon/Interaction-networks_tensorflow which had consulted the first author for some implementation details.