hrishivish23/MPM-Verse-MaterialSim-Large
MPM-Verse-MaterialSim-Large Dataset Summary This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, and jelly. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations. The dataset is rendered using five geometric models - Stanford-bunny, Spot, Dragon, Armadillo, and Blub. Each setting has 10 trajectories… See the full description on the dataset page: https://huggingface.co/datasets/hrishivish23/MPM-Verse-MaterialSim-Large.
| Author / organization | hrishivish23 |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 420 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 13 March 2025 |
| Last updated at source | 13 March 2025 |
Popularity, last 3 days
420 now (+79 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 16 Oct 2026 Description: MPM-Verse-MaterialSim-Large Dataset Summary This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, and jelly. Each material is represented as point-clouds that evolve over tim → MPM-Verse-MaterialSim-Large Dataset Summary This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, and jelly. Each material is represented as point-clouds that evolve over tim
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