Datasets:

jongwonryu
/

TIDE-TIDES

---
pretty_name: TIDE/TIDES - Interactive 4D Volumetric Liquid Forecasting (Sphere Subset)
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
tags:
- 3d
- timeseries
- fluid-dynamics
- liquid-forecasting
- computational-physics
- sph
- arxiv:2610.16167
configs:
- config_name: sphere
  default: true
  data_files:
  - split: train
    path: index/sphere/train.parquet
  - split: validation
    path: index/sphere/val.parquet
  - split: test
    path: index/sphere/test.parquet
  - split: test_ood
    path: index/sphere/test_ood.parquet
- config_name: sphere_size_ood
  data_files:
  - split: test_ood
    path: index/sphere_size_ood/test_ood.parquet
---
# TIDE / TIDES: Initial Sphere Dataset Release

Dataset artifacts for **Interactive 4D Volumetric Liquid Forecasting under
Moving-Solid Interaction**, by **Jongwon Ryu, Sunjae Yoon, and Junyeong Kim**.

[Paper](https://arxiv.org/abs/2610.16167) |
[Framework code](https://github.com/jongwonryu/TIDE-TIDES)

## Release Scope

**This is an initial partial release of the locally preserved sphere data,
not the complete benchmark reported in the paper.** It contains 200 main
episodes and 40 separate sphere size-OOD episodes. Cube/cone data and the full
paper-scale episode counts are not included. This subset alone does not
reproduce the paper's aggregate tables. Journal-extension datasets and
pretrained checkpoints are not part of this release.

See `RELEASE_STATUS.json` for upload completion. While its status is
`upload_in_progress`, some indexed binary files may not yet be downloadable.

The original dense and particle `.npz` files and rigid-pose CSVs are preserved
byte-for-byte. Split assignments are unchanged. JSON metadata has been made
portable by replacing server-specific paths with repository-relative paths.
`release_manifest.json` and `SHA256SUMS` record the binary file inventory and
SHA-256 digests. The duplicate v1 processing version is intentionally excluded.

## Available Episodes

| Configuration | Train | Validation | Test | Motion OOD | Size OOD |
| --- | ---: | ---: | ---: | ---: | ---: |
| `sphere` | 128 | 16 | 16 | 40 | - |
| `sphere_size_ood` | - | - | - | - | 40 |

Original manifests use `val`; the Hugging Face metadata index exposes that split
as `validation`. Both OOD configurations use `test_ood`, distinguished by the
configuration name. Each record describes a complete simulation episode,
not an individual frame or an independently generated rollout window.

Dense grids are **32 x 128 x 128** in depth/height/width order, sampled at
**8 FPS**. Episode lengths vary; consult `num_frames`. The main sphere radius
is 4 voxels; size-OOD spheres use 8 voxels. Coordinates and voxel spacing are
stored per episode; do not assume identical world bounds across cases.

## Load the Lightweight Episode Index

```python
from datasets import load_dataset
episodes = load_dataset("jongwonryu/TIDE-TIDES", "sphere")
record = episodes["train"][0]
print(record["episode_id"], record["file"], record["num_frames"])
size_ood = load_dataset("jongwonryu/TIDE-TIDES", "sphere_size_ood")
```

**`load_dataset` downloads episode metadata, not the dense velocity tensors.**
The Hub viewer likewise browses the episode index rather than rendering the
3D flow. Download the selected NPZ explicitly:
```python
import numpy as np
from huggingface_hub import hf_hub_download
path = hf_hub_download(
    repo_id="jongwonryu/TIDE-TIDES",
    repo_type="dataset",
    filename=record["file"],
)
with np.load(path, allow_pickle=False) as episode:
    initial_velocity = episode["init_flow"]
    dt = episode["dt"]
    spacing = episode["voxel_size_xyz"]
```
NPZ members are decompressed when accessed; full sequences can require several
GB of RAM. Opening an archive does not require loading every array. Original
arrays are retained rather than silently changing precision or resampling.
## Download for the Released Training Code
```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="jongwonryu/TIDE-TIDES",
    repo_type="dataset",
    local_dir="TIDE-TIDES-data",
)
```
The complete partial release, including dense samples and particle exports,
is approximately **152 GB** (decimal). Download individual episodes or use
`allow_patterns` if storage is limited. For the framework's existing loaders,
set `dataset.root` to `TIDE-TIDES-data/dualsph_liquid_v2_dense` for the main
subset or `TIDE-TIDES-data/dualsph_liquid_v3_dense_sizeood2x_testood` for size OOD.
Retain the `raw/` folder when particle supervision is needed; portable manifest
references resolve to that folder.
## Files and Array Conventions
```text
dualsph_liquid_v2_dense/
  manifest.jsonl
  summary.json
  samples/{train,val,test,test_ood}/*.npz
dualsph_liquid_v3_dense_sizeood2x_testood/
  manifest.jsonl
  summary.json
  samples/test_ood/*.npz
raw/<episode_id>/sim/
  water_surface_state.npz
  rigid_poses.csv
index/{sphere,sphere_size_ood}/
  *.parquet
schema.json
release_manifest.json
SHA256SUMS
LICENSE
```
Important dense NPZ members (`T` is the saved-frame count):
| Array | Shape | Description |
| --- | --- | --- |
| `target_flow` | `[T,3,D,H,W]` | Dense liquid velocity in xyz component order |
| `init_flow` | `[3,D,H,W]` | Initial velocity |
| `target_velocity_voxel` | `[T,D,H,W,3]` | Velocity in voxels per saved frame |
| `target_velocity_world` | `[T,D,H,W,3]` | Velocity in world distance per second |
| `cond_flow` | `[T,3,D,H,W]` | Solid velocity conditioning |
| `cond_volume` | `[T,1,D,H,W]` | Solid conditioning volume |
| `particle_support_mask` | `[T,D,H,W]` | Native particle-to-grid support |
| `particle_support_velocity_world` | `[T,D,H,W,3]` | Particle-supported velocity |
| `fluid_mask` | `[T,D,H,W]` | Benchmark-defined dense liquid mask |
| `phase_onehot` | `[T,D,H,W,2]` | Liquid/solid phase labels; no air channel |
| `object_occupancy` | `[T,D,H,W]` | Solid occupancy |
| `object_center_world` | `[T,3]` | Realized solid centers |
| `object_velocity_world` | `[T,3]` | Realized solid velocities |
| `bounds_xyz`, `voxel_size_xyz`, `dt` | Per-episode metadata | World bounds, spacing, frame interval |
The velocity conversion is
`voxel_velocity = world_velocity * dt / voxel_size_xyz`.
Do not apply `dt` a second time when advecting in voxel/frame coordinates.
`schema.json` contains representative complete array schemas for each
configuration. Particle exports and rigid poses provide the preserved
simulation-derived observations; solver executables and native binary dumps
are not distributed here.
## Intended Use and Limitations
The task is **conditional liquid forecasting under a known future solid-state
sequence**, not closed-loop two-way fluid-solid simulation. These simulations
supply realized solid motion; the forecaster does not update solid dynamics.
Partition by entire episodes and preserve the supplied splits before extracting
windows. Never provide future ground-truth liquid states as inference inputs.
Dense fields are constructed from SPH particle exports through particle-to-grid
conversion and dense-domain filling. They are **not solver-native continuum
fields**. Native particle support and filled liquid occupancy are different
quantities; use `particle_support_mask` when reporting support-restricted
metrics. This release contains only sphere geometry and does not establish
generalization to cube/cone, real-world liquids, or new journal scenarios.
The released framework configurations are runnable starting points, not a
guarantee of reproducing every paper/rebuttal schedule on this partial subset.
## License and Attribution
The dataset is released under **Creative Commons Attribution 4.0 International
(CC BY 4.0)**. See `LICENSE`. Attribute Jongwon Ryu, Sunjae Yoon, and Junyeong Kim,
cite the paper, and identify any modifications. Framework code is distributed
separately in the linked GitHub repository.
```bibtex
@article{ryu2026interactive,
  title={Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction},
  author={Ryu, Jongwon and Yoon, Sunjae and Kim, Junyeong},
  journal={arXiv preprint arXiv:2610.16167},
  year={2026}
}
```

	---
	pretty_name: TIDE/TIDES - Interactive 4D Volumetric Liquid Forecasting (Sphere Subset)
	license: cc-by-4.0
	language:
	- en
	size_categories:
	- n<1K
	tags:
	- 3d
	- timeseries
	- fluid-dynamics
	- liquid-forecasting
	- computational-physics
	- sph
	- arxiv:2610.16167
	configs:
	- config_name: sphere
	default: true
	data_files:
	- split: train
	path: index/sphere/train.parquet
	- split: validation
	path: index/sphere/val.parquet
	- split: test
	path: index/sphere/test.parquet
	- split: test_ood
	path: index/sphere/test_ood.parquet
	- config_name: sphere_size_ood
	data_files:
	- split: test_ood
	path: index/sphere_size_ood/test_ood.parquet
	---

	# TIDE / TIDES: Initial Sphere Dataset Release

	Dataset artifacts for **Interactive 4D Volumetric Liquid Forecasting under
	Moving-Solid Interaction, by Jongwon Ryu, Sunjae Yoon, and Junyeong Kim**.

	[Paper](https://arxiv.org/abs/2610.16167) \|
	[Framework code](https://github.com/jongwonryu/TIDE-TIDES)

	## Release Scope

	**This is an initial partial release of the locally preserved sphere data,
	not the complete benchmark reported in the paper.** It contains 200 main
	episodes and 40 separate sphere size-OOD episodes. Cube/cone data and the full
	paper-scale episode counts are not included. This subset alone does not
	reproduce the paper's aggregate tables. Journal-extension datasets and
	pretrained checkpoints are not part of this release.

	See `RELEASE_STATUS.json` for upload completion. While its status is
	`upload_in_progress`, some indexed binary files may not yet be downloadable.

	The original dense and particle `.npz` files and rigid-pose CSVs are preserved
	byte-for-byte. Split assignments are unchanged. JSON metadata has been made
	portable by replacing server-specific paths with repository-relative paths.
	`release_manifest.json` and `SHA256SUMS` record the binary file inventory and
	SHA-256 digests. The duplicate v1 processing version is intentionally excluded.

	## Available Episodes

	\| Configuration \| Train \| Validation \| Test \| Motion OOD \| Size OOD \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| `sphere` \| 128 \| 16 \| 16 \| 40 \| - \|
	\| `sphere_size_ood` \| - \| - \| - \| - \| 40 \|

	Original manifests use `val`; the Hugging Face metadata index exposes that split
	as `validation`. Both OOD configurations use `test_ood`, distinguished by the
	configuration name. Each record describes a complete simulation episode,
	not an individual frame or an independently generated rollout window.

	Dense grids are 32 x 128 x 128 in depth/height/width order, sampled at
	8 FPS. Episode lengths vary; consult `num_frames`. The main sphere radius
	is 4 voxels; size-OOD spheres use 8 voxels. Coordinates and voxel spacing are
	stored per episode; do not assume identical world bounds across cases.

	## Load the Lightweight Episode Index

	```python
	from datasets import load_dataset

	episodes = load_dataset("jongwonryu/TIDE-TIDES", "sphere")
	record = episodes["train"][0]
	print(record["episode_id"], record["file"], record["num_frames"])

	size_ood = load_dataset("jongwonryu/TIDE-TIDES", "sphere_size_ood")
	```

	`load_dataset` downloads episode metadata, not the dense velocity tensors.
	The Hub viewer likewise browses the episode index rather than rendering the
	3D flow. Download the selected NPZ explicitly:

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

	path = hf_hub_download(
	repo_id="jongwonryu/TIDE-TIDES",
	repo_type="dataset",
	filename=record["file"],
	)
	with np.load(path, allow_pickle=False) as episode:
	initial_velocity = episode["init_flow"]
	dt = episode["dt"]
	spacing = episode["voxel_size_xyz"]
	```

	NPZ members are decompressed when accessed; full sequences can require several
	GB of RAM. Opening an archive does not require loading every array. Original
	arrays are retained rather than silently changing precision or resampling.

	## Download for the Released Training Code

	```python
	from huggingface_hub import snapshot_download

	snapshot_download(
	repo_id="jongwonryu/TIDE-TIDES",
	repo_type="dataset",
	local_dir="TIDE-TIDES-data",
	)
	```

	The complete partial release, including dense samples and particle exports,
	is approximately 152 GB (decimal). Download individual episodes or use
	`allow_patterns` if storage is limited. For the framework's existing loaders,
	set `dataset.root` to `TIDE-TIDES-data/dualsph_liquid_v2_dense` for the main
	subset or `TIDE-TIDES-data/dualsph_liquid_v3_dense_sizeood2x_testood` for size OOD.
	Retain the `raw/` folder when particle supervision is needed; portable manifest
	references resolve to that folder.

	## Files and Array Conventions

	```text
	dualsph_liquid_v2_dense/
	manifest.jsonl
	summary.json
	samples/{train,val,test,test_ood}/*.npz
	dualsph_liquid_v3_dense_sizeood2x_testood/
	manifest.jsonl
	summary.json
	samples/test_ood/*.npz
	raw/<episode_id>/sim/
	water_surface_state.npz
	rigid_poses.csv
	index/{sphere,sphere_size_ood}/
	*.parquet
	schema.json
	release_manifest.json
	SHA256SUMS
	LICENSE
	```

	Important dense NPZ members (`T` is the saved-frame count):

	\| Array \| Shape \| Description \|
	\| --- \| --- \| --- \|
	\| `target_flow` \| `[T,3,D,H,W]` \| Dense liquid velocity in xyz component order \|
	\| `init_flow` \| `[3,D,H,W]` \| Initial velocity \|
	\| `target_velocity_voxel` \| `[T,D,H,W,3]` \| Velocity in voxels per saved frame \|
	\| `target_velocity_world` \| `[T,D,H,W,3]` \| Velocity in world distance per second \|
	\| `cond_flow` \| `[T,3,D,H,W]` \| Solid velocity conditioning \|
	\| `cond_volume` \| `[T,1,D,H,W]` \| Solid conditioning volume \|
	\| `particle_support_mask` \| `[T,D,H,W]` \| Native particle-to-grid support \|
	\| `particle_support_velocity_world` \| `[T,D,H,W,3]` \| Particle-supported velocity \|
	\| `fluid_mask` \| `[T,D,H,W]` \| Benchmark-defined dense liquid mask \|
	\| `phase_onehot` \| `[T,D,H,W,2]` \| Liquid/solid phase labels; no air channel \|
	\| `object_occupancy` \| `[T,D,H,W]` \| Solid occupancy \|
	\| `object_center_world` \| `[T,3]` \| Realized solid centers \|
	\| `object_velocity_world` \| `[T,3]` \| Realized solid velocities \|
	\| `bounds_xyz`, `voxel_size_xyz`, `dt` \| Per-episode metadata \| World bounds, spacing, frame interval \|

	The velocity conversion is
	`voxel_velocity = world_velocity * dt / voxel_size_xyz`.
	Do not apply `dt` a second time when advecting in voxel/frame coordinates.
	`schema.json` contains representative complete array schemas for each
	configuration. Particle exports and rigid poses provide the preserved
	simulation-derived observations; solver executables and native binary dumps
	are not distributed here.

	## Intended Use and Limitations

	The task is **conditional liquid forecasting under a known future solid-state
	sequence**, not closed-loop two-way fluid-solid simulation. These simulations
	supply realized solid motion; the forecaster does not update solid dynamics.
	Partition by entire episodes and preserve the supplied splits before extracting
	windows. Never provide future ground-truth liquid states as inference inputs.

	Dense fields are constructed from SPH particle exports through particle-to-grid
	conversion and dense-domain filling. They are **not solver-native continuum
	fields**. Native particle support and filled liquid occupancy are different
	quantities; use `particle_support_mask` when reporting support-restricted
	metrics. This release contains only sphere geometry and does not establish
	generalization to cube/cone, real-world liquids, or new journal scenarios.

	The released framework configurations are runnable starting points, not a
	guarantee of reproducing every paper/rebuttal schedule on this partial subset.

	## License and Attribution

	The dataset is released under **Creative Commons Attribution 4.0 International
	(CC BY 4.0)**. See `LICENSE`. Attribute Jongwon Ryu, Sunjae Yoon, and Junyeong Kim,
	cite the paper, and identify any modifications. Framework code is distributed
	separately in the linked GitHub repository.

	```bibtex
	@article{ryu2026interactive,
	title={Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction},
	author={Ryu, Jongwon and Yoon, Sunjae and Kim, Junyeong},
	journal={arXiv preprint arXiv:2610.16167},
	year={2026}
	}
	```