Add dataset card with figures (banner, gallery, augmentation methodology)

README.md

@@ -1,44 +1,189 @@
 ---
 license: odc-by
-pretty_name: DeepJEB++
+pretty_name: "DeepJEB++"
 size_categories:
   - 10K<n<100K
+task_categories:
+  - tabular-regression
+  - graph-ml
 tags:
   - engineering-design
   - finite-element-analysis
-  - surrogate-modeling
-  - 3d-geometry
+  - structural-mechanics
+  - 3d
+  - mesh
+  - generative-design
+  - foundation-model
   - jet-engine-bracket
+  - surrogate-modeling
 ---
 
-# DeepJEB++
+<div align="center">
+  <img src="assets/banner_displacement.png" alt="DeepJEB++ generated brackets — displacement fields" width="100%">
+</div>
 
-A foundation-model-driven 2D-to-3D data-augmentation dataset for structural
-engineering design. **15,360** simulation-labeled jet-engine brackets, generated
-by adapting Stable Diffusion v1.5 and TRELLIS, each paired with automatically
-computed linear-elastic finite-element labels under four load cases.
+<h1 align="center">DeepJEB++</h1>
+<p align="center"><b>Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation</b></p>
 
-## License
-**ODC-By v1.0** (Open Data Commons Attribution License). Free to use, modify,
-and share -- including for commercial purposes -- with attribution (please cite
-the DeepJEB++ paper and the upstream SimJEB and DeepJEB datasets).
-See the `LICENSE` file.
-
-## Contents
-| Path | Description |
-|------|-------------|
-| `1_surface_meshes/` | 15,360 surface meshes (`.obj`, native ~50k verts) |
-| `2_boundary_conditions/` | Four-bolt + loaded-clevis interfaces (vertex indices, mm frame) |
-| `3_fea_fields/` | Per-case surface FEA fields (`.npz`): displacement `U`, von Mises `vm` for 4 loads |
-| `deepjebpp_labels.csv` | Scalar labels: mass, per-load max displacement & 95th-pct von Mises |
-| `metadata.json` | Material, load cases, units, layout |
-| `README.txt` | Full human-readable specification |
-
-All field/BC entries are at a consistent 25k FEM-mesh density. BC node indices
-reference `3_fea_fields/<case>.npz` `surface_points` (25k), not the native `.obj`.
-
-## Load cases (Ti-6Al-4V, linear static)
-vertical 35.6 kN | horizontal 37.8 kN | diagonal 42.3 kN | torsional 565 kN-mm
+<p align="center">
+  <a href="https://arxiv.org/abs/2606.12994"><img src="https://img.shields.io/badge/arXiv-2606.12994-b31b1b.svg" alt="arXiv"></a>
+  <img src="https://img.shields.io/badge/license-ODC--By%201.0-blue.svg" alt="License: ODC-By 1.0">
+  <img src="https://img.shields.io/badge/designs-15%2C360-ff9d00.svg" alt="15,360 designs">
+  <img src="https://img.shields.io/badge/load%20cases-4-3b4a8c.svg" alt="4 load cases">
+</p>
+
+> **DeepJEB++** is a large-scale dataset of **generatively-designed jet-engine brackets**, each paired with
+> physics-based performance labels from an automated finite-element (FEA) pipeline. It is built by **augmenting
+> the SimJEB design space inside a 2D latent space** and lifting the synthesized images to 3D with a **3D
+> foundation model (TRELLIS)**, then automatically recovering boundary conditions and solving four structural
+> load cases. The result couples **geometry ↔ physics** at a scale (40× SimJEB) suitable for data-driven and
+> surrogate modelling in engineering design.
+
+Authors: Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang — **KAIST SmartDesignLab**
+
+| | |
+|---|---|
+| **Designs (deployable)** | **15,360** |
+| **Load cases** | vertical / horizontal / diagonal / torsional |
+| **Per design** | surface mesh · boundary conditions · FEA surface fields · scalar labels (incl. mass) |
+| **Material** | Ti-6Al-4V · E = 113,800 MPa · ν = 0.342 |
+| **Scale** | 40× SimJEB (380) |
+| **Paper** | [arXiv:2606.12994](https://arxiv.org/abs/2606.12994) |
+| **License** | ODC-By 1.0 (matching upstream SimJEB / DeepJEB) |
+
+---
+
+## 1 · The data, qualitatively
+
+<div align="center">
+  <img src="assets/gallery.png" alt="Generated bracket variety with auto-detected interfaces" width="92%">
+  <br><sub><b>Generated bracket variety + auto-detected interfaces</b> — 24 of 15,360, each with a gate-validated 4-bolt flange and lug-clevis detection (orange).</sub>
+</div>
+
+<br>
+
+<div align="center">
+  <img src="assets/fea_fields.png" alt="Four-load FEA response fields" width="92%">
+  <br><sub><b>4-load FEA response fields.</b> Top: displacement (deformed ×9). Bottom: von Mises stress. Columns: vertical / horizontal / diagonal / torsional.</sub>
+</div>
+
+The hero banner above shows real brackets coloured by their **per-case vertical-load displacement field**
+(blue = clamped bolts, red = lug tip). The same brackets, as raw geometry:
+
+<div align="center">
+  <img src="assets/banner_geometry.png" alt="Generated bracket meshes (geometry)" width="100%">
+</div>
+
+---
+
+## 2 · Augmentation methodology
+
+The core idea is **2D latent-space augmentation**: instead of perturbing 3D meshes directly, new designs are
+synthesized by **interpolating between SimJEB seed brackets in the latent space of a fine-tuned diffusion
+model**, then reconstructed in 3D by a foundation model and labelled by FEA.
+
+| # | Step | What happens |
+|---|------|--------------|
+| 1 | **Seed pairs** | Pairs of SimJEB bracket renders chosen as interpolation endpoints. |
+| 2 | **2D latent interpolation** | Fine-tuned Stable Diffusion mixes the two VAE latents (ratio 0→1) → frames IS00–IS18. |
+| 3 | **Image → 3D** | A single diagonal view drives TRELLIS (SimJEB-finetuned) image-to-3D, 25-step. |
+| 4 | **Automatic BC** | 4-bolt flange + lug-clevis detected and validated by a calibrated gate. |
+| 5 | **FEA labels** | Four load cases solved → displacement, von Mises, mass per design. |
+
+<div align="center">
+  <img src="assets/framework.png" alt="End-to-end framework" width="92%">
+  <br><sub><b>End-to-end framework</b> — generation (latent interpolation + foundation-model lifting) → automatic labelling.</sub>
+</div>
+
+<br>
+
+<div align="center">
+  <img src="assets/interp_2d.png" alt="2D latent interpolation" width="80%">
+  <br><sub><b>2D latent interpolation</b> — a smooth transition between two parent brackets (IS00 → IS18).</sub>
+</div>
+
+> **Why 2D-latent augmentation?** Interpolating in a learned image latent space produces smooth, valid,
+> manufacturable-looking new brackets that span the design space between real examples — far easier than
+> perturbing 3D meshes directly — while a 3D foundation model guarantees consistent, watertight geometry ready
+> for FEA. A key finding: increasing the diffusion sampling steps raised valid BC-detection from **16% → 96%**.
+
+---
+
+## 3 · What each sample contains
+
+Every design shares one `<case>` id (e.g. `012-015-diag_xz_mm_IS02`) across mesh, boundary conditions, fields
+and the label row.
+
+```
+DeepJEB-PP/
+├── 1_surface_meshes.tar.gz       # 15,360 × <case>.obj   — input surface mesh (~50k verts)
+├── 2_boundary_conditions.tar.gz  # 15,360 × <case>.npz   — bolt_idx (clamped), lug_idx (loaded), bolt_holes
+├── 3_fea_fields.tar.gz           # 15,360 × <case>.npz   — surface_points/faces, per-load U & von Mises (×4)
+├── deepjebpp_labels.csv          # mass_g, vol_mm3, per-load max|u|, p95 von Mises, ...
+└── metadata.json                 # material / loads / units / schema
+```
+
+> BC node indices reference the 25k FEM `surface_points` frame in `3_fea_fields/<case>.npz` (not the native `.obj`).
+
+**FEA specification**
+
+| | |
+|---|---|
+| Material | Ti-6Al-4V · E = 113,800 MPa · ν = 0.342 (yield 903 MPa / 131 ksi, reference) |
+| Vertical (ver) | force (0, 0, 1) · 35,600 N |
+| Horizontal (hor) | force (−1, 0, 0) · 37,800 N |
+| Diagonal (dia) | force (−0.669, 0, 0.743) · 42,300 N |
+| Torsional (tor) | moment (0, 1, 0) · 565,000 N·mm |
+| Solver | tetgen + conjugate-gradient, 25k node budget |
+
+---
+
+## 4 · Usage
+
+The dataset ships as per-component `.tar.gz` archives + a CSV. Download, extract, then load per case.
+
+```bash
+# Download everything (recommended)
+huggingface-cli download KAIST-SmartDesignLab/DeepJEB-PP --repo-type dataset --local-dir DeepJEB-PP
+cd DeepJEB-PP && for f in *.tar.gz; do tar -xzf "$f"; done
+```
+
+```python
+import numpy as np, pandas as pd, trimesh
+
+case  = "012-015-diag_xz_mm_IS02"
+mesh  = trimesh.load(f"1_surface_meshes/{case}.obj")
+bc    = np.load(f"2_boundary_conditions/{case}.npz")   # bolt_idx, lug_idx
+field = np.load(f"3_fea_fields/{case}.npz")            # ver_U, ver_vm, hor_U, ...
+label = pd.read_csv("deepjebpp_labels.csv").set_index("case").loc[case]
+
+clamped = field["surface_points"][bc["bolt_idx"]]      # clamped bolt nodes (mm)
+vm_ver  = field["ver_vm"]                              # vertical-load von Mises (MPa)
+```
+
+Single-file download via `huggingface_hub`:
+
+```python
+from huggingface_hub import hf_hub_download
+p = hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "3_fea_fields.tar.gz", repo_type="dataset")
+```
+
+---
 
 ## Citation
-Citation will be added upon publication (ASME Journal of Mechanical Design).
+
+```bibtex
+@article{deepjebpp2026,
+  title   = {DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering
+             Dataset via 2D Latent Space Augmentation},
+  author  = {Yoo, Soyoung and Jeong, Leekyo and Ra, Jinsu and Lee, Dongeon
+             and Yang, Sunwoong and Jeong, Hyogu and Kang, Namwoo},
+  journal = {arXiv preprint arXiv:2606.12994},
+  year    = {2026}
+}
+```
+
+## License
+
+Released under the **Open Data Commons Attribution License (ODC-By v1.0)**, matching the upstream
+SimJEB / DeepJEB datasets. Derived from the SimJEB dataset (GE Jet Engine Bracket Challenge geometry).