| ---
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| license: odc-by
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| pretty_name: "DeepJEB++"
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| size_categories:
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| - 10K<n<100K
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| task_categories:
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| - tabular-regression
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| - graph-ml
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| tags:
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| - engineering-design
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| - finite-element-analysis
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| - structural-mechanics
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| - 3d
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| - mesh
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| - generative-design
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| - foundation-model
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| - jet-engine-bracket
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| - surrogate-modeling
|
| ---
|
|
|
| <div align="center">
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| <img src="assets/banner_displacement.png" alt="DeepJEB++ generated brackets — displacement fields" width="100%">
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| </div>
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|
|
| <h1 align="center">DeepJEB++</h1>
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| <p align="center"><b>Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation</b></p>
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|
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| <p align="center">
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| <a href="https://arxiv.org/abs/2606.12994"><img src="https://img.shields.io/badge/arXiv-2606.12994-b31b1b.svg" alt="arXiv"></a>
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| <a href="https://huggingface.co/datasets/KAIST-SmartDesignLab/DeepJEB-PP"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-ffb000.svg" alt="Hugging Face Dataset"></a>
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| <img src="https://img.shields.io/badge/Status-Under%20Review-orange.svg" alt="Status: Under Review">
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| <img src="https://img.shields.io/badge/license-ODC--By%201.0-blue.svg" alt="License: ODC-By 1.0">
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| </p>
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|
|
| <p align="center">Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang — <b>KAIST SmartDesignLab</b></p>
|
|
|
| ---
|
|
|
| ## Contents
|
|
|
| - [News](#news)
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| - [Overview](#overview)
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| - [The data, qualitatively](#the-data-qualitatively)
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| - [Augmentation methodology](#augmentation-methodology)
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| - [Dataset structure](#dataset-structure)
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| - [Usage](#usage)
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| - [Applications](#applications)
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| - [Citation](#citation)
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| - [Acknowledgements](#acknowledgements)
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| - [License](#license)
|
|
|
| ---
|
|
|
| ## News
|
|
|
| - **2026-07 — v1.1.** The torsional moment is now applied about the **Z-axis** `(0, 0, 1)` to match the SimJEB reference torsion condition (v1.0 applied it about the Y-axis), and `tor_maxvm` was added to the labels. Vertical / horizontal / diagonal loads, geometry, meshes and boundary conditions are unchanged. If you already downloaded v1.0, apply the lightweight [torsion patch](#usage) instead of re-downloading the full ~29 GB (pin `revision="v1.0"` for the original).
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| - **2026-06** — DeepJEB++ released on Hugging Face: **15,360** designs with surface meshes, boundary conditions, per-load FEA surface fields, and scalar labels (incl. mass).
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| - **2026-06** — Preprint on arXiv ([2606.12994](https://arxiv.org/abs/2606.12994)); manuscript **under review**.
|
|
|
| ---
|
|
|
| ## Overview
|
|
|
| > **DeepJEB++** is a large-scale dataset of **generatively-designed jet-engine brackets**, each paired with
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| > physics-based performance labels from an automated finite-element (FEA) pipeline. It is built by **augmenting
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| > the SimJEB design space inside a 2D latent space** and lifting the synthesized images to 3D with a **3D
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| > foundation model (TRELLIS)**, then automatically recovering boundary conditions and solving four structural
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| > load cases. The result couples **geometry ↔ physics** at a scale (40× SimJEB) suitable for data-driven and
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| > surrogate modelling in engineering design.
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|
|
| <div align="center">
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| <img src="assets/teaser.gif" alt="A generated bracket rotating, coloured by its vertical-load displacement field" width="46%">
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| <br><sub>A single design, coloured by its vertical-load displacement field (blue = clamped bolts, red = lug tip).</sub>
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| </div>
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|
|
| | | |
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| |---|---|
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| | **Designs (deployable)** | 15,360 |
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| | **Load cases** | vertical / horizontal / diagonal / torsional |
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| | **Per design** | surface mesh · boundary conditions · FEA surface fields · scalar labels (incl. mass) |
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| | **Material** | Ti-6Al-4V · E = 113,800 MPa · ν = 0.342 |
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| | **Scale** | 40× SimJEB (380) |
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| | **Paper** | [arXiv:2606.12994](https://arxiv.org/abs/2606.12994) |
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| | **License** | ODC-By 1.0 (matching upstream SimJEB / DeepJEB) |
|
|
|
| ---
|
|
|
| ## The data, qualitatively
|
|
|
| <div align="center">
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| <img src="assets/gallery.png" alt="Generated bracket variety with auto-detected interfaces" width="92%">
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| <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>
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| </div>
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|
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| <br>
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|
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| <div align="center">
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| <img src="assets/fea_fields.png" alt="Four-load FEA response fields" width="92%">
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| <br><sub><b>4-load FEA response fields.</b> Top: displacement (deformed ×9). Bottom: von Mises stress. Columns: vertical / horizontal / diagonal / torsional.</sub>
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| </div>
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|
|
| The hero banner shows real brackets coloured by their per-case vertical-load displacement field. The same
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| brackets, as raw geometry:
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|
|
| <div align="center">
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| <img src="assets/banner_geometry.png" alt="Generated bracket meshes (geometry)" width="100%">
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| </div>
|
|
|
| ---
|
|
|
| ## Augmentation methodology
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|
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| The core idea is **2D latent-space augmentation**: instead of perturbing 3D meshes directly, new designs are
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| synthesized by **interpolating between SimJEB seed brackets in the latent space of a fine-tuned diffusion
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| model**, then reconstructed in 3D by a foundation model and labelled by FEA.
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|
|
| | # | Step | What happens |
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| |---|------|--------------|
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| | 1 | **Seed pairs** | Pairs of SimJEB bracket renders chosen as interpolation endpoints. |
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| | 2 | **2D latent interpolation** | Fine-tuned Stable Diffusion mixes the two VAE latents (ratio 0→1) → frames IS00–IS18. |
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| | 3 | **Image → 3D** | A single diagonal view drives TRELLIS (SimJEB-finetuned) image-to-3D, 25-step. |
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| | 4 | **Automatic BC** | 4-bolt flange + lug-clevis detected and validated by a calibrated gate. |
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| | 5 | **FEA labels** | Four load cases solved → displacement, von Mises, mass per design. |
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|
|
| <div align="center">
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| <img src="assets/framework.png" alt="End-to-end framework" width="92%">
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| <br><sub><b>End-to-end framework</b> — generation (latent interpolation + foundation-model lifting) → automatic labelling.</sub>
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| </div>
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|
|
| <br>
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|
|
| <div align="center">
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| <img src="assets/interp_2d.png" alt="2D latent interpolation" width="80%">
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| <br><sub><b>2D latent interpolation</b> — a smooth transition between two parent brackets (IS00 → IS18).</sub>
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| </div>
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|
|
| > **Why 2D-latent augmentation?** Interpolating in a learned image latent space produces smooth, valid,
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| > manufacturable-looking new brackets that span the design space between real examples — far easier than
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| > perturbing 3D meshes directly — while a 3D foundation model guarantees consistent, watertight geometry ready
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| > for FEA. A key finding: increasing the diffusion sampling steps raised valid BC-detection from **16% → 96%**.
|
|
|
| ---
|
|
|
| ## Dataset structure
|
|
|
| Distributed as per-component `.tar.gz` archives + a CSV. Every design shares one `<case>` id
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| (e.g. `012-015-diag_xz_mm_IS02`) across all modalities.
|
|
|
| ```
|
| DeepJEB-PP/
|
| ├── 1_surface_meshes.tar.gz # 15,360 × <case>.obj
|
| ├── 2_boundary_conditions.tar.gz # 15,360 × <case>.npz
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| ├── 3_fea_fields.tar.gz # 15,360 × <case>.npz
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| ├── deepjebpp_labels.csv # scalar labels (15,360 rows)
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| └── metadata.json # material / loads / units / schema
|
| ```
|
|
|
| **Modalities**
|
|
|
| | Modality | File | Content |
|
| |---|---|---|
|
| | Geometry | `1_surface_meshes/<case>.obj` | input surface mesh, native ~50k verts |
|
| | Boundary conditions | `2_boundary_conditions/<case>.npz` | `bolt_idx` (clamped), `lug_idx` (loaded), `bolt_holes` — indices into the 25k FEM `surface_points` |
|
| | FEA surface fields | `3_fea_fields/<case>.npz` | `surface_points` (N,3), `surface_faces` (M,3), and per load `{ver,hor,dia,tor}_U` (N,3) + `_vm` (N,) |
|
| | Scalar labels | `deepjebpp_labels.csv` | `mass_g`, `vol_mm3`, per-load `max|u|`, `p95 von Mises`, … |
|
|
|
| **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 |
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| | Diagonal (dia) | force (−0.669, 0, 0.743) · 42,300 N |
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| | Torsional (tor) | moment (0, 0, 1) · 565,000 N·mm _(Z-axis, matches SimJEB; v1.1)_ |
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| | Solver | tetgen + conjugate-gradient, 25k node budget |
|
|
|
| ---
|
|
|
| ## Usage
|
|
|
| **Download & extract**
|
|
|
| ```bash
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| huggingface-cli download KAIST-SmartDesignLab/DeepJEB-PP --repo-type dataset --local-dir DeepJEB-PP
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| cd DeepJEB-PP && for f in *.tar.gz; do tar -xzf "$f"; done
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| ```
|
|
|
| **Already have v1.0? Apply the torsion patch (no full re-download)**
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|
|
| v1.1 changes **only the torsion labels** (moment axis Y→Z) and adds `tor_maxvm`; all other loads, geometry, meshes and BCs are byte-identical to v1.0. Update in place instead of re-downloading the ~29 GB `3_fea_fields`:
|
|
|
| ```python
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| from huggingface_hub import hf_hub_download
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| # ~14 GB torsion-only patch
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| hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP",
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| "3_fea_fields_torsion_z_v1.1.tar.gz",
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| repo_type="dataset", local_dir="patch")
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| hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "apply_torsion_patch.py",
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| repo_type="dataset", local_dir="patch")
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| ```
|
|
|
| ```bash
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| mkdir -p patch/torsion
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| tar -xzf patch/3_fea_fields_torsion_z_v1.1.tar.gz -C patch/torsion
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| python patch/apply_torsion_patch.py <your 3_fea_fields dir> patch/torsion
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| ```
|
|
|
| The script swaps `tor_U / tor_vm / tor_maxu / tor_p95vm` and adds `tor_maxvm` in each `<case>.npz` (verifying `surface_points` match). Then also replace the small `deepjebpp_labels.csv` and `metadata.json` with the v1.1 copies. See `PATCH_README.txt`.
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|
|
| **Pin a version**
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|
|
| ```python
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| from huggingface_hub import snapshot_download
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| snapshot_download("KAIST-SmartDesignLab/DeepJEB-PP", repo_type="dataset",
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| revision="v1.1") # or "v1.0" for the original Y-axis torsion
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| ```
|
|
|
| **Load one design**
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|
|
| ```python
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| import numpy as np, pandas as pd, trimesh
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|
|
| case = "012-015-diag_xz_mm_IS02"
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| mesh = trimesh.load(f"1_surface_meshes/{case}.obj")
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| bc = np.load(f"2_boundary_conditions/{case}.npz") # bolt_idx, lug_idx
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| field = np.load(f"3_fea_fields/{case}.npz") # ver_U, ver_vm, hor_U, ...
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| label = pd.read_csv("deepjebpp_labels.csv").set_index("case").loc[case]
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|
|
| clamped = field["surface_points"][bc["bolt_idx"]] # clamped bolt nodes (mm)
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| vm_ver = field["ver_vm"] # vertical-load von Mises (MPa)
|
| ```
|
|
|
| **PyTorch dataloader** (geometry + fields + scalar targets)
|
|
|
| ```python
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| import os, glob, numpy as np, pandas as pd, torch
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| from torch.utils.data import Dataset
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|
|
| class DeepJEBPP(Dataset):
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| """Per-case surface points, BC masks, per-load fields, and scalar labels."""
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| LOADS = ["ver", "hor", "dia", "tor"]
|
|
|
| def __init__(self, root, load="ver"):
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| self.root, self.load = root, load
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| self.cases = sorted(os.path.splitext(os.path.basename(f))[0]
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| for f in glob.glob(f"{root}/3_fea_fields/*.npz"))
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| self.labels = pd.read_csv(f"{root}/deepjebpp_labels.csv").set_index("case")
|
|
|
| def __len__(self):
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| return len(self.cases)
|
|
|
| def __getitem__(self, i):
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| c = self.cases[i]
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| fld = np.load(f"{self.root}/3_fea_fields/{c}.npz")
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| bc = np.load(f"{self.root}/2_boundary_conditions/{c}.npz")
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| pts = fld["surface_points"].astype("float32")
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| n = len(pts)
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| bolt = np.zeros(n, "float32"); bolt[bc["bolt_idx"]] = 1.0 # clamped mask
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| lug = np.zeros(n, "float32"); lug[bc["lug_idx"]] = 1.0 # loaded mask
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| row = self.labels.loc[c]
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| return {
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| "case": c,
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| "points": torch.from_numpy(pts), # (N,3) mm
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| "bc": torch.from_numpy(np.stack([bolt, lug], 1)), # (N,2)
|
| "U": torch.from_numpy(fld[f"{self.load}_U"].astype("float32")), # (N,3)
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| "vm": torch.from_numpy(fld[f"{self.load}_vm"].astype("float32")), # (N,)
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| "y": torch.tensor([row["mass_g"],
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| row[f"{self.load}_p95vm"],
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| row[f"{self.load}_maxu"]], dtype=torch.float32),
|
| }
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|
|
| # ds = DeepJEBPP("DeepJEB-PP", load="ver"); print(len(ds), ds[0]["points"].shape)
|
| ```
|
|
|
| ---
|
|
|
| ## Applications
|
|
|
| - **Surrogate modelling** — learn geometry → performance (mass, p95 von Mises, peak displacement, or full
|
| nodal fields) with point-cloud / mesh-GNN / implicit models; a 40× larger training corpus than SimJEB.
|
| - **Field prediction** — predict per-node displacement and stress fields under each of the four load cases.
|
| - **Generative & inverse design** — benchmark generators on a labelled, BC-aware bracket design space; close
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| the loop with the released solver-input meshes.
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| - **Design optimisation** — data-driven optimisation / constraint screening using the mass and stress labels.
|
| - **Cross-dataset transfer** — pre-train on DeepJEB++ and transfer to the smaller real SimJEB / DeepJEB sets.
|
|
|
| ---
|
|
|
| ## 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},
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| journal = {arXiv preprint arXiv:2606.12994},
|
| year = {2026}
|
| }
|
| ```
|
|
|
| ---
|
|
|
| ## Acknowledgements
|
|
|
| DeepJEB++ builds on the **SimJEB** dataset (Whalen et al.) and the original **DeepJEB**, both derived from the
|
| **GE Jet Engine Bracket Challenge** geometry, and uses the **TRELLIS** 3D foundation model for image-to-3D
|
| generation. Developed at **KAIST SmartDesignLab**.
|
|
|
| ---
|
|
|
| ## 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).
|
|
|
