Datasets:

KAIST-SmartDesignLab
/

DeepJEB-PP

@@ -1,316 +1,318 @@
----
-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>
-  <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>
-  <img src="https://img.shields.io/badge/Status-Under%20Review-orange.svg" alt="Status: Under Review">
-  <img src="https://img.shields.io/badge/license-ODC--By%201.0-blue.svg" alt="License: ODC-By 1.0">
-</p>
-<p align="center">Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang &nbsp;—&nbsp; <b>KAIST SmartDesignLab</b></p>
----
-## Contents
-- [News](#news)
-- [Overview](#overview)
-- [The data, qualitatively](#the-data-qualitatively)
-- [Augmentation methodology](#augmentation-methodology)
-- [Dataset structure](#dataset-structure)
-- [Usage](#usage)
-- [Applications](#applications)
-- [Citation](#citation)
-- [Acknowledgements](#acknowledgements)
-- [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).
-- **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).
-- **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
-> 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.
-<div align="center">
-  <img src="assets/teaser.gif" alt="A generated bracket rotating, coloured by its vertical-load displacement field" width="46%">
-  <br><sub>A single design, coloured by its vertical-load displacement field (blue = clamped bolts, red = lug tip).</sub>
-</div>
-| | |
-|---|---|
-| **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) |
----
-## 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 shows real brackets coloured by their per-case vertical-load displacement field. The same
-brackets, as raw geometry:
-<div align="center">
-  <img src="assets/banner_geometry.png" alt="Generated bracket meshes (geometry)" width="100%">
-</div>
----
-## 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%**.
----
-## Dataset structure
-Distributed as per-component `.tar.gz` archives + a CSV. Every design shares one `<case>` id
-(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
-├── 3_fea_fields.tar.gz           # 15,360 × <case>.npz
-├── deepjebpp_labels.csv          # scalar labels (15,360 rows)
-└── 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 |
-| Diagonal (dia) | force (−0.669, 0, 0.743) · 42,300 N |
-| Torsional (tor) | moment (0, 0, 1) · 565,000 N·mm  _(Z-axis, matches SimJEB; v1.1)_ |
-| Solver | tetgen + conjugate-gradient, 25k node budget |
----
-## Usage
-**Download & extract**
-```bash
-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
-```
-**Already have v1.0? Apply the torsion patch (no full re-download)**
-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
-from huggingface_hub import hf_hub_download
-# ~14 GB torsion-only patch
-hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP",
-                "3_fea_fields_torsion_z_v1.1.tar.gz",
-                repo_type="dataset", local_dir="patch")
-hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "apply_torsion_patch.py",
-                repo_type="dataset", local_dir="patch")
-```
-```bash
-mkdir -p patch/torsion
-tar -xzf patch/3_fea_fields_torsion_z_v1.1.tar.gz -C patch/torsion
-python patch/apply_torsion_patch.py  <your 3_fea_fields dir>  patch/torsion
-```
-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`.
-**Pin a version**
-```python
-from huggingface_hub import snapshot_download
-snapshot_download("KAIST-SmartDesignLab/DeepJEB-PP", repo_type="dataset",
-                  revision="v1.1")   # or "v1.0" for the original Y-axis torsion
-```
-**Load one design**
-```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)
-```
-**PyTorch dataloader** (geometry + fields + scalar targets)
-```python
-import os, glob, numpy as np, pandas as pd, torch
-from torch.utils.data import Dataset
-class DeepJEBPP(Dataset):
-    """Per-case surface points, BC masks, per-load fields, and scalar labels."""
-    LOADS = ["ver", "hor", "dia", "tor"]
-    def __init__(self, root, load="ver"):
-        self.root, self.load = root, load
-        self.cases = sorted(os.path.splitext(os.path.basename(f))[0]
-                            for f in glob.glob(f"{root}/3_fea_fields/*.npz"))
-        self.labels = pd.read_csv(f"{root}/deepjebpp_labels.csv").set_index("case")
-    def __len__(self):
-        return len(self.cases)
-    def __getitem__(self, i):
-        c = self.cases[i]
-        fld = np.load(f"{self.root}/3_fea_fields/{c}.npz")
-        bc  = np.load(f"{self.root}/2_boundary_conditions/{c}.npz")
-        pts = fld["surface_points"].astype("float32")
-        n   = len(pts)
-        bolt = np.zeros(n, "float32"); bolt[bc["bolt_idx"]] = 1.0   # clamped mask
-        lug  = np.zeros(n, "float32"); lug[bc["lug_idx"]]  = 1.0    # loaded mask
-        row  = self.labels.loc[c]
-        return {
-            "case": c,
-            "points": torch.from_numpy(pts),                       # (N,3) mm
-            "bc": torch.from_numpy(np.stack([bolt, lug], 1)),      # (N,2)
-            "U":  torch.from_numpy(fld[f"{self.load}_U"].astype("float32")),   # (N,3)
-            "vm": torch.from_numpy(fld[f"{self.load}_vm"].astype("float32")),  # (N,)
-            "y":  torch.tensor([row["mass_g"],
-                                row[f"{self.load}_p95vm"],
-                                row[f"{self.load}_maxu"]], dtype=torch.float32),
-        }
-# 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
-  the loop with the released solver-input meshes.
-- **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},
-  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).

+---
+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>
+  <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>
+  <img src="https://img.shields.io/badge/Status-Under%20Review-orange.svg" alt="Status: Under Review">
+  <img src="https://img.shields.io/badge/license-ODC--By%201.0-blue.svg" alt="License: ODC-By 1.0">
+</p>
+<p align="center">Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang &nbsp;—&nbsp; <b>KAIST SmartDesignLab</b></p>
+> **📦 Dataset size & viewer note.** DeepJEB++ contains **15,360** deployable, simulation-labeled brackets. The Hugging Face Dataset Viewer above shows only a small preview because the FEA field data are distributed as a compressed archive (`3_fea_fields.tar.gz`) rather than a columnar Parquet file, so the viewer cannot stream the full table. To access all 15,360 records, download the archive or use the provided loader (see **Usage** below).
+---
+## Contents
+- [News](#news)
+- [Overview](#overview)
+- [The data, qualitatively](#the-data-qualitatively)
+- [Augmentation methodology](#augmentation-methodology)
+- [Dataset structure](#dataset-structure)
+- [Usage](#usage)
+- [Applications](#applications)
+- [Citation](#citation)
+- [Acknowledgements](#acknowledgements)
+- [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).
+- **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).
+- **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
+> 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.
+<div align="center">
+  <img src="assets/teaser.gif" alt="A generated bracket rotating, coloured by its vertical-load displacement field" width="46%">
+  <br><sub>A single design, coloured by its vertical-load displacement field (blue = clamped bolts, red = lug tip).</sub>
+</div>
+| | |
+|---|---|
+| **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) |
+---
+## 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 shows real brackets coloured by their per-case vertical-load displacement field. The same
+brackets, as raw geometry:
+<div align="center">
+  <img src="assets/banner_geometry.png" alt="Generated bracket meshes (geometry)" width="100%">
+</div>
+---
+## 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%**.
+---
+## Dataset structure
+Distributed as per-component `.tar.gz` archives + a CSV. Every design shares one `<case>` id
+(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
+├── 3_fea_fields.tar.gz           # 15,360 × <case>.npz
+├── deepjebpp_labels.csv          # scalar labels (15,360 rows)
+└── 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 |
+| Diagonal (dia) | force (−0.669, 0, 0.743) · 42,300 N |
+| Torsional (tor) | moment (0, 0, 1) · 565,000 N·mm  _(Z-axis, matches SimJEB; v1.1)_ |
+| Solver | tetgen + conjugate-gradient, 25k node budget |
+---
+## Usage
+**Download & extract**
+```bash
+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
+```
+**Already have v1.0? Apply the torsion patch (no full re-download)**
+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
+from huggingface_hub import hf_hub_download
+# ~14 GB torsion-only patch
+hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP",
+                "3_fea_fields_torsion_z_v1.1.tar.gz",
+                repo_type="dataset", local_dir="patch")
+hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "apply_torsion_patch.py",
+                repo_type="dataset", local_dir="patch")
+```
+```bash
+mkdir -p patch/torsion
+tar -xzf patch/3_fea_fields_torsion_z_v1.1.tar.gz -C patch/torsion
+python patch/apply_torsion_patch.py  <your 3_fea_fields dir>  patch/torsion
+```
+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`.
+**Pin a version**
+```python
+from huggingface_hub import snapshot_download
+snapshot_download("KAIST-SmartDesignLab/DeepJEB-PP", repo_type="dataset",
+                  revision="v1.1")   # or "v1.0" for the original Y-axis torsion
+```
+**Load one design**
+```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)
+```
+**PyTorch dataloader** (geometry + fields + scalar targets)
+```python
+import os, glob, numpy as np, pandas as pd, torch
+from torch.utils.data import Dataset
+class DeepJEBPP(Dataset):
+    """Per-case surface points, BC masks, per-load fields, and scalar labels."""
+    LOADS = ["ver", "hor", "dia", "tor"]
+    def __init__(self, root, load="ver"):
+        self.root, self.load = root, load
+        self.cases = sorted(os.path.splitext(os.path.basename(f))[0]
+                            for f in glob.glob(f"{root}/3_fea_fields/*.npz"))
+        self.labels = pd.read_csv(f"{root}/deepjebpp_labels.csv").set_index("case")
+    def __len__(self):
+        return len(self.cases)
+    def __getitem__(self, i):
+        c = self.cases[i]
+        fld = np.load(f"{self.root}/3_fea_fields/{c}.npz")
+        bc  = np.load(f"{self.root}/2_boundary_conditions/{c}.npz")
+        pts = fld["surface_points"].astype("float32")
+        n   = len(pts)
+        bolt = np.zeros(n, "float32"); bolt[bc["bolt_idx"]] = 1.0   # clamped mask
+        lug  = np.zeros(n, "float32"); lug[bc["lug_idx"]]  = 1.0    # loaded mask
+        row  = self.labels.loc[c]
+        return {
+            "case": c,
+            "points": torch.from_numpy(pts),                       # (N,3) mm
+            "bc": torch.from_numpy(np.stack([bolt, lug], 1)),      # (N,2)
+            "U":  torch.from_numpy(fld[f"{self.load}_U"].astype("float32")),   # (N,3)
+            "vm": torch.from_numpy(fld[f"{self.load}_vm"].astype("float32")),  # (N,)
+            "y":  torch.tensor([row["mass_g"],
+                                row[f"{self.load}_p95vm"],
+                                row[f"{self.load}_maxu"]], dtype=torch.float32),
+        }
+# 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
+  the loop with the released solver-input meshes.
+- **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},
+  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).