Datasets:
language:
- en
pretty_name: Discrete Elastic Rods Simulation Dataset
tags:
- physics
- simulation
- synthetic-data
- graph-neural-networks
- gnn
- geometric-deep-learning
- physics-informed-machine-learning
- regression
- computational-physics
- deformable-objects
- discrete-elastic-rods
- dynamics
- physical-simulation
- computer-graphics
- trajectory-prediction
task_categories:
- graph-ml
- time-series-forecasting
task_ids:
- multivariate-time-series-forecasting
size_categories:
- 1M<n<10M
license: apache-2.0
Discrete Elastic Rods Simulation Dataset
Dataset Description
This dataset contains synthetic data generated from Discrete Elastic Rods (DER) simulations, a physical model used to represent deformable slender structures such as hair strands, ropes, cables, and elastic fibers.
The dataset was generated frame-by-frame during physical simulations and stores geometric, kinematic, and dynamic properties for each rod vertex.
The primary objective of this dataset is to support machine learning research involving:
- Graph Neural Networks (GNNs)
- Physics-informed learning
- Dynamics prediction
- Physical regression
- Deformable object simulation
Each sample corresponds to a vertex at a specific simulation frame.
Dataset Structure
The dataset is divided into three splits:
| Split | Samples |
|---|---|
| Train | 4,265,580 |
| Validation | 376,200 |
| Test | 460,920 |
Features
| Feature | Description |
|---|---|
frame |
Simulation frame index |
strand |
Rod/strand identifier |
vertex_id |
Vertex identifier |
pos_x/y/z |
Vertex position |
vel_x/y/z |
Vertex velocity |
force_x/y/z |
Applied forces |
curvature |
Local curvature |
torsion |
Local torsion |
prev_segment_direction |
Previous segment direction vector |
next_segment_direction |
Next segment direction vector |
prev_segment_length |
Previous segment length |
next_segment_length |
Next segment length |
boundary |
Boundary condition information |
Data Generation
The dataset was generated using a Discrete Elastic Rods simulation environment with randomized physical parameters and dynamic interactions.
The simulations include temporal evolution of elastic rods under physical constraints and external/internal forces.
Intended Use
This dataset is intended for:
- Training Graph Neural Networks
- Physics regression tasks
- Simulation approximation
- Temporal dynamics prediction
- Deformable object learning
Limitations
- The dataset is fully synthetic.
- Results may not perfectly generalize to real-world rod dynamics.
- Physical behavior depends on the simulation assumptions and parameter ranges.
License
- apache-2.0
Citation
@dataset{der_simulation_dataset,
title={Discrete Elastic Rods Simulation Dataset},
author={Samuel Ferreira Santos},
year={2026}
}