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README.md

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 ---
 license: mit
+pretty_name: Hybrid Helmholtz const_back raw data
+tags:
+- geophysics
+- helmholtz
+- wave-scattering
+- operator-learning
 ---
+
+# 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](https://arxiv.org/abs/2602.11197).
+
+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
+
+```bash
+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
+```

const_back/pressure_sharp.npy

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+size 26214400128

const_back/pressure_smooth.npy

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+oid sha256:be999b13f7c1cf508e0225cba60ba2798766b26f3a93a74fb97147f3683f2837
+size 26214400128

const_back/velocity_sharp.npy

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+size 13107200128

const_back/velocity_smooth.npy

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+oid sha256:3543eb1178e36630882a7f1b524cb65b89d68ea195d101c6269584cc5e073033
+size 13107200128

hf_release_manifest.json

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+{
+  "artifact_type": "raw_data",
+  "created_at_utc": "2026-06-02T19:30:09.006793+00:00",
+  "dataset": "const_back",
+  "files": [
+    {
+      "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_smooth.npy",
+      "shape": [
+        50000,
+        256,
+        256
+      ],
+      "size": "24.4 GB",
+      "size_bytes": 26214400128
+    },
+    {
+      "dtype": "float32",
+      "kind": "raw_numpy_array",
+      "path": "const_back/velocity_sharp.npy",
+      "shape": [
+        50000,
+        256,
+        256
+      ],
+      "size": "12.2 GB",
+      "size_bytes": 13107200128
+    },
+    {
+      "dtype": "float32",
+      "kind": "raw_numpy_array",
+      "path": "const_back/velocity_smooth.npy",
+      "shape": [
+        50000,
+        256,
+        256
+      ],
+      "size": "12.2 GB",
+      "size_bytes": 13107200128
+    }
+  ],
+  "layout": "const_back/<raw-array>.npy",
+  "paper": {
+    "title": "Hybrid operator learning of wave scattering maps in high-contrast media",
+    "url": "https://arxiv.org/abs/2602.11197"
+  },
+  "selection_policy": "Only the four raw arrays consumed by scripts/prepare_data.py are staged."
+}