s3no-benchmark/suboff_data

Suboff Dataset

This public dataset contains the Suboff training split and the public validation inputs used by the leaderboard. It is intended for model training, local inference, and end-to-end leaderboard submission.

The data represents steady-state surface pressure over parameterized submarine hull geometries. Each geometry is evaluated under multiple Reynolds-number conditions. The model input is an irregular surface point cloud plus flow-condition features; the prediction target is pressure or pressure coefficient at each surface point.

Dataset Contents

data/
+-- train/
    +-- <geometry-id>-<condition-id>/
        +-- all_zones_combined.npy
+-- validation_input/
    +-- <geometry-id>-<condition-id>/
        +-- all_zones_combined.npy

This split contains:

Split	Label availability	Geometries	Conditions per geometry	Files
Train	Public pressure labels	158	6	948
Validation input	No pressure labels	39	6	234

Training .npy files are approximately 8 MB each. Validation-input .npy files are smaller because the pressure column has been removed.

File Format

Training sample files are NumPy arrays:

all_zones_combined.npy: float64 array with shape [N, 5]

The five columns are:

Column	Name	Description
0	x	Surface point x-coordinate
1	y	Surface point y-coordinate
2	z	Surface point z-coordinate
3	pressure	CFD pressure value at the surface point
4	zone_id	Integer-like surface zone identifier

Validation input files are NumPy arrays:

all_zones_combined.npy: float64 array with shape [N, 4]

The four validation-input columns are:

Column	Name	Description
0	x	Surface point x-coordinate
1	y	Surface point y-coordinate
2	z	Surface point z-coordinate
3	zone_id	Integer-like surface zone identifier

The point count N can vary by sample. These files are irregular surface point clouds and should not be treated as structured grids.

Flow Conditions

The condition ID is encoded in the folder name suffix, for example:

hull-tail-fuyi-r-l005-r01-sail-bottom-tail-1p3-top-h001-3p97e7

Here 3p97e7 denotes the flow condition. The current loader maps these condition IDs to Reynolds numbers and inlet velocities:

Condition ID	Reynolds number	Velocity
1p32e7	13,200,000	3.0507
2p23e7	22,300,000	5.1444
2p64e7	26,400,000	6.0962
3p10e7	31,000,000	7.1611
3p57e7	35,700,000	8.2311
3p97e7	39,700,000	9.1520

Model Input and Target

A typical model consumes:

point features: [x, y, z, zone_id]
global features: [reynolds_number, velocity]

The training target can be the raw pressure column or the pressure coefficient:

Cp = pressure / (0.5 * rho * velocity^2)

The reference loader currently uses:

rho = 998.2

Loading Example

from pathlib import Path
import re
import numpy as np

ROOT = Path("data/train")

REYNOLDS_TO_VELOCITY = {
    "1p32e7": 3.0507,
    "2p23e7": 5.1444,
    "2p64e7": 6.0962,
    "3p10e7": 7.1611,
    "3p57e7": 8.2311,
    "3p97e7": 9.1520,
}


def parse_condition(sample_dir_name: str):
    match = re.search(r"-(\d+p\d+e\d+)$", sample_dir_name)
    if match is None:
        raise ValueError(f"Cannot parse condition from {sample_dir_name}")
    condition_id = match.group(1)
    reynolds = float(condition_id.replace("p", "."))
    velocity = REYNOLDS_TO_VELOCITY[condition_id]
    return condition_id, reynolds, velocity


sample_file = next(ROOT.glob("*/all_zones_combined.npy"))
sample_name = sample_file.parent.name
condition_id, reynolds, velocity = parse_condition(sample_name)

array = np.load(sample_file)
xyz = array[:, :3].astype("float32")
pressure = array[:, 3].astype("float32")
zone_id = array[:, 4].astype("int64")

rho = 998.2
cp = pressure / (0.5 * rho * velocity**2)

print(sample_name)
print(array.shape, array.dtype)
print(condition_id, reynolds, velocity)
print(xyz.shape, pressure.shape, zone_id.shape, cp.shape)

Leaderboard Submission Format

Download the files under data/validation_input/, run local inference, and submit a ZIP file to the Space:

submission.zip
+-- predictions/
    +-- <geometry-id>-<condition-id>.npy

Each prediction file must contain predicted pressure coefficient values:

pred_cp: float array with shape [N] or [N, 1]

The point order must match the corresponding data/validation_input/<sample-id>/all_zones_combined.npy file exactly.

Public and Private Data Boundary

This repository is public. It contains training files with pressure values and validation-input files without pressure values. The private validation-truth files are stored separately in s3no-benchmark/suboff_val and should not be copied into this public repository.

For a leaderboard workflow:

• Public train data can include coordinates, zone IDs, condition features, and pressure/Cp labels.
• Private validation or test truth should include labels but remain inaccessible to participants.
• Public validation/test inputs should omit pressure/Cp labels if they are used for hidden scoring.

License

This dataset repository is released under the Apache License 2.0 as indicated in the Dataset Card metadata.

If you use this data in a benchmark, report the exact split and the condition mapping above so results remain reproducible.