| --- |
| 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 |
|
|
| ```bibtex |
| @dataset{der_simulation_dataset, |
| title={Discrete Elastic Rods Simulation Dataset}, |
| author={Samuel Ferreira Santos}, |
| year={2026} |
| } |
| ``` |
