Card: DrivAerNet-style — teaser GIF, TOC, News, modalities, dataloader, applications, acknowledgements

README.md

@@ -32,6 +32,34 @@ tags:
   <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
+
+- **2026-06** — 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
@@ -39,11 +67,14 @@ tags:
 > 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**
+<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** |
+| **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 |
@@ -53,7 +84,7 @@ Authors: Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang
 
 ---
 
-## 1 · The data, qualitatively
+## The data, qualitatively
 
 <div align="center">
   <img src="assets/gallery.png" alt="Generated bracket variety with auto-detected interfaces" width="92%">
@@ -67,8 +98,8 @@ Authors: Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang
   <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:
+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%">
@@ -76,7 +107,7 @@ The hero banner above shows real brackets coloured by their **per-case vertical-
 
 ---
 
-## 2 · Augmentation methodology
+## 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
@@ -109,21 +140,28 @@ model**, then reconstructed in 3D by a foundation model and labelled by FEA.
 
 ---
 
-## 3 · What each sample contains
+## Dataset structure
 
-Every design shares one `<case>` id (e.g. `012-015-diag_xz_mm_IS02`) across mesh, boundary conditions, fields
-and the label row.
+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   — 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, ...
+├── 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
 ```
 
-> BC node indices reference the 25k FEM `surface_points` frame in `3_fea_fields/<case>.npz` (not the native `.obj`).
+**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**
 
@@ -138,16 +176,17 @@ DeepJEB-PP/
 
 ---
 
-## 4 · Usage
+## Usage
 
-The dataset ships as per-component `.tar.gz` archives + a CSV. Download, extract, then load per case.
+**Download & extract**
 
 ```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
 ```
 
+**Load one design**
+
 ```python
 import numpy as np, pandas as pd, trimesh
 
@@ -161,15 +200,62 @@ 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`:
+**PyTorch dataloader** (geometry + fields + scalar targets)
 
 ```python
-from huggingface_hub import hf_hub_download
-p = hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "3_fea_fields.tar.gz", repo_type="dataset")
+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
@@ -183,6 +269,16 @@ p = hf_hub_download("KAIST-SmartDesignLab/DeepJEB-PP", "3_fea_fields.tar.gz", re
 }
 ```
 
+---
+
+## 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