---
license: cc-by-sa-4.0
tags:
- physics-simulation
- n-body
- scientific-machine-learning
- visual-reasoning
---

# N-Body Trajectories for Visual Reasoning Research

## Dataset Description

This dataset contains a collection of simulated trajectories for a 3-body gravitational system. It was generated to train and evaluate "Visual Reasoning LoRA" (VR-LoRA) models as described in the paper: **"Visual Reasoning Transfer: Leveraging Pretrained Visual Models for Physical and Temporal Prediction"**.

The core idea of the research is to encode these physical state vectors into "spatial field images" and train a vision model to predict the temporal evolution of the system.

*   **Research Project Repository:** [https://github.com/sandner-art/SC-Visual-Reasoning](https://github.com/sandner-art/SC-Visual-Reasoning)
*   **Trained Model (LoRA):** [huggingface.co/sandner/vr-lora-physics-sd15](https://huggingface.co/sandner/vr-lora-physics-sd15)

### How to Use this Dataset

The dataset is provided as compressed NumPy `.npz` files. It can be loaded directly with NumPy.

```python
import numpy as np
from huggingface_hub import hf_hub_download

# Download the training data
file_path = hf_hub_download(
    repo_id="sandner/n-body-trajectories-for-vrlora", # Replace with your repo name
    filename="nbody_3_train_10000.npz",
    repo_type="dataset"
)

# Load the trajectories
data = np.load(file_path)
trajectories = data['trajectories']

# The shape is (num_samples, num_timesteps, num_particles, state_dim)
# e.g., (10000, 50, 3, 4)
print(trajectories.shape)

# Each state is a vector of [x, y, vx, vy]
first_trajectory_first_step = trajectories
print(first_trajectory_first_step)
```

### Dataset Structure

The repository contains two primary files:
*   `nbody_3_train_10000.npz`: The main training set with 10,000 trajectories.
*   `nbody_3_test_500.npz`: A smaller test set with 500 trajectories for evaluation.

Each trajectory consists of 50 timesteps for a 3-particle system. Each particle's state at each timestep is represented by a 4-dimensional vector: `[position_x, position_y, velocity_x, velocity_y]`.

### Dataset Creation

The data was generated using a custom Python script (`generate_dataset.py` in the main GitHub repo) that utilizes `scipy.integrate.solve_ivp` with a Runge-Kutta (RK45) method.

Key simulation parameters:
*   **Gravitational Constant (G):** 1.0
*   **Time Span:** 0.0 to 2.0
*   **Softening Factor:** 1e-3 (to prevent numerical singularities)
*   **Initial Conditions:** Particle positions, velocities, and masses were sampled from uniform random distributions.

## Citing this Dataset

If you use this dataset in your research, please cite our paper (BibTeX entry coming soon) and link back to this repository.