davidMis/hybrid-operator-learning-of-wave-scattering-maps-in-high-contrast-media
Updated
artifact_type string | created_at_utc string | dataset string | files list | layout string | paper dict | selection_policy string |
|---|---|---|---|---|---|---|
raw_data | 2026-06-02T19:30:09.006793+00:00 | const_back | [
{
"dtype": "complex64",
"kind": "raw_numpy_array",
"path": "const_back/pressure_sharp.npy",
"shape": [
50000,
256,
256
],
"size": "24.4 GB",
"size_bytes": 26214400128
},
{
"dtype": "complex64",
"kind": "raw_numpy_array",
"path": "const_back/pressure_smoo... | const_back/<raw-array>.npy | {
"title": "Hybrid operator learning of wave scattering maps in high-contrast media",
"url": "https://arxiv.org/abs/2602.11197"
} | Only the four raw arrays consumed by scripts/prepare_data.py are staged. |
Hybrid Helmholtz const_back Raw Data
This dataset repository contains the raw NumPy arrays used by the paper Hybrid operator learning of wave scattering maps in high-contrast media.
The repository is intentionally minimal. It contains only the four raw arrays
consumed by scripts/prepare_data.py; processed splits can be regenerated from
these files.
Files
| Path | Dtype | Shape | Size |
|---|---|---|---|
const_back/velocity_sharp.npy |
float32 |
[50000, 256, 256] |
12.2 GB |
const_back/velocity_smooth.npy |
float32 |
[50000, 256, 256] |
12.2 GB |
const_back/pressure_sharp.npy |
complex64 |
[50000, 256, 256] |
24.4 GB |
const_back/pressure_smooth.npy |
complex64 |
[50000, 256, 256] |
24.4 GB |
Usage
hf download dmis09/hybrid-operator-learning-of-wave-scattering-maps-in-high-contrast-media --repo-type dataset --local-dir data/raw
python scripts/prepare_data.py --raw-root data/raw --output-root data/processed --dataset const_back
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Models trained or fine-tuned on davidMis/hybrid-operator-learning-of-wave-scattering-maps-in-high-contrast-media