| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - emmi-ai |
| - physics-simulation |
| - dem |
| - cfd-dem |
| - neural-operator |
| pretty_name: NeuralDEM Dataset |
| --- |
| |
| # NeuralDEM Dataset |
|
|
| Dataset repository for **NeuralDEM**, containing simulation data for training and evaluating deep learning surrogates for industrial particulate flows and particle-fluid coupled systems. |
|
|
| ## Dataset Summary |
|
|
| The NeuralDEM dataset covers two primary physics benchmarks: |
|
|
| 1. **Hopper Simulations (Particle Systems)** |
| * **Setup**: Hopper domain with a bottom outlet initially loaded with ~250,000 particles discharging over time. |
| * **Physics**: Discrete Element Method (DEM) dynamics across diverse hopper geometry angles and particle friction regimes. |
| 2. **Fluidized Bed Reactor (Particle-Fluid Coupled Systems)** |
| * **Setup**: Reactor containing ~500,000 particles with uniform fluid (air) injection from the bottom grid. |
| * **Physics**: Coupled CFD-DEM multi-physics system over ~160,000 hexahedral CFD grid cells across varying fluid inlet velocities. |
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|
| For inference scripts, model checkpoints, and simulation rollouts, visit the [NeuralDEM GitHub Repository](https://github.com/Emmi-AI/NeuralDEM). |
|
|
| ## License |
|
|
| This dataset is distributed under the [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. |
|
|
| ## Citation instructions |
| ``` |
| @article{alkin2024neuraldem, |
| title={{NeuralDEM} for real time simulations of industrial particular flows}, |
| author={Benedikt Alkin and Tobias Kronlachner and Samuele Papa and Stefan Pirker and Thomas Lichtenegger and Johannes Brandstetter}, |
| journal={Nature Communications Physics}, |
| year={2025} |
| doi={10.1038/s42005-025-02342-4}, |
| } |
| ``` |
