---
license: odc-by
pretty_name: "DeepJEB++"
size_categories:
  - 10K<n<100K
task_categories:
  - tabular-regression
  - graph-ml
tags:
  - engineering-design
  - finite-element-analysis
  - structural-mechanics
  - 3d
  - mesh
  - generative-design
  - foundation-model
  - jet-engine-bracket
  - surrogate-modeling
---

<div align="center">
  <img src="assets/banner_displacement.png" alt="DeepJEB++ generated brackets — displacement fields" width="100%">
</div>

<h1 align="center">DeepJEB++</h1>
<p align="center"><b>Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation</b></p>

<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

```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).