Neural Force Field

Learning Generalized Physical Representation from a Few Examples

Project structure

NeuralForceField/
│
├── data/
│   └──iphyre/game_seq_data/
│       ├── activated_pendulum/
│       ├── angle/
│       ├── ...
│       └── support_hole/
│   └── nbody/
│       ├── train.npy
│       ├── within.npy
│       └── cross.npy
│
├── checkpoints/          
│
├── iphyre/             
│   ├── configs/         # Configuration files
│   ├── models/          # Model dir containing NFF, IN, SlotFormer
│   ├── utils/           # Useful tools such as dataloader
│   ├── planning.py      # Planning script
│   ├── README.md        # An instruction for use
│   ├── test.py          # Evaluation functions
│   └── train.py         # Training functions
│
├── iphyre/             
│   ├── configs/         # Configuration files
│   ├── models/          # Model dir containing NFF, IN, SlotFormer
│   ├── utils/           # Useful tools such as dataloader
│   ├── generate_data.py # Data generation functions
│   ├── planning.py      # Planning script
│   ├── README.md        # An instruction for use
│   ├── test.py          # Evaluation functions
│   └── train.py         # Training functions

Getting started

Make sure you have installed torch, torchdiffeq, iphyre, and rebound.

Go to the specific task directory to train and test the models. The instructions of running commands are provided for each task (README_iphyre and README_nbody). Download data here and checkpoints here.

cd ./iphyre

or

cd ./nbody