v1.1: torsion moment axis Y->Z (SimJEB-matching) — data card, metadata, labels, torsion patch script

PATCH_README.txt

@@ -0,0 +1,12 @@
+DeepJEB++ torsion patch (v1.0 -> v1.1)
+======================================
+WHAT: Corrects the torsion moment axis from Y to Z (matching the SimJEB
+reference torsion condition) and adds tor_maxvm. Only torsion fields change;
+vertical/horizontal/diagonal loads and geometry are identical to v1.0.
+
+If you already downloaded v1.0 you do NOT need to re-download the full
+3_fea_fields (~29 GB). Instead:
+  1. Download this patch archive (3_fea_fields_torsion_z_v1.1.tar.gz, ~7 GB) and extract it.
+  2. Run:  python apply_torsion_patch.py  <your 3_fea_fields dir>  <extracted patch dir>
+  3. Replace deepjebpp_labels.csv and metadata.json with the v1.1 copies (small files).
+New users can simply download the full v1.1 release instead.

README.md

@@ -1,285 +1,99 @@
 ---
 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
-  - structural-mechanics
-  - 3d
-  - mesh
-  - generative-design
-  - foundation-model
-  - jet-engine-bracket
   - surrogate-modeling
+  - 3d-geometry
+  - jet-engine-bracket
 ---
 
-<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>
+> 📢 Update — v1.1 (2026-07)
+> The torsional load moment is now applied about the Z-axis 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, you do not need to re-download the full
+> ~29 GB — apply the lightweight torsion patch (see [Usage](#usage)).
 
-<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>
+# DeepJEB++
 
-<p align="center">Soyoung Yoo · Leekyo Jeong · Jinsu Ra · Dongeon Lee · Sunwoong Yang · Hyogu Jeong · Namwoo Kang &nbsp;—&nbsp; <b>KAIST SmartDesignLab</b></p>
+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.
 
----
+## 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
-
-- [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
-> 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, 1, 0) · 565,000 N·mm |
-| Solver | tetgen + conjugate-gradient, 25k node budget |
-
----
+| 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`.
 
 ## 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
+### New users — full v1.1
+```python
+from huggingface_hub import snapshot_download
+snapshot_download("KAIST-SmartDesignLab/DeepJEB-PP", repo_type="dataset",
+                  local_dir="DeepJEB-PP")
 ```
-
-**Load one design**
-
+Extract the `.tar.gz` archives, load fields with `numpy.load(...)` and scalars
+from `deepjebpp_labels.csv`.
+
+### Already have v1.0? Apply the torsion patch (no 29 GB re-download)
+v1.1 changes **only the torsion labels** (moment axis Y -> Z to match the SimJEB
+reference torsion condition) and adds `tor_maxvm`. Vertical / horizontal /
+diagonal loads, geometry, meshes and boundary conditions are byte-identical to
+v1.0. To update in place without re-downloading the full `3_fea_fields` (~29 GB):
 ```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)
+from huggingface_hub import hf_hub_download
+# ~14 GB torsion-only patch instead of the full archive
+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 replaces `tor_U / tor_vm / tor_maxu / tor_p95vm` and adds
+`tor_maxvm` in each `<case>.npz` (verifying `surface_points` match), leaving
+the other loads untouched. Then also replace the small `deepjebpp_labels.csv`
+and `metadata.json` with the v1.1 copies. See `PATCH_README.txt`.
 
-**PyTorch dataloader** (geometry + fields + scalar targets)
-
+### Pin a specific version
 ```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)
+snapshot_download("KAIST-SmartDesignLab/DeepJEB-PP", repo_type="dataset",
+                  revision="v1.1")   # or "v1.0" for the original Y-axis torsion
 ```
 
----
+## Load cases (Ti-6Al-4V, linear static)
+vertical 35.6 kN | horizontal 37.8 kN | diagonal 42.3 kN | torsional 565 kN-mm
 
-## Applications
+The **torsional** load is applied as a moment about the **Z-axis** `(0, 0, 1)`, matching the SimJEB reference torsion condition. (See Version history.)
 
-- **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.
-
----
+## Version history
+- **v1.1** (2026-07) - Torsional moment axis corrected from Y to **Z-axis** `(0,0,1)` to match the SimJEB reference torsion loading condition; `tor_maxvm` (full-volume max von Mises) added to `deepjebpp_labels.csv`. **Recommended version.**
+- **v1.0** (2026-06) - Initial release; torsional moment applied about the Y-axis `(0,1,0)`. Retained via git tag for reproducibility of any prior results.
 
 ## 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).
+Citation will be added upon publication (ASME Journal of Mechanical Design).

README.txt

@@ -35,7 +35,7 @@ BC:         4 bolt holes clamped (Dirichlet), load applied on the lug
   ver  force   ( 0.000, 0.0, 1.000)    35,600 N
   hor  force   (-1.000, 0.0, 0.000)    37,800 N
   dia  force   (-0.669, 0.0, 0.743)    42,300 N
-  tor  moment  ( 0.000, 1.0, 0.000)   565,000 N*mm
+  tor  moment  ( 0.000, 0.0, 1.000)   565,000 N*mm
 
 --------------------------------------------------------------------------------
 FIELD NPZ CONTENTS  (numpy .npz, np.load)

apply_torsion_patch.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+"""
+DeepJEB++ v1.0 -> v1.1 torsion patch.
+Corrects the torsion moment axis (Y -> Z, matching the SimJEB reference torsion
+condition) and adds `tor_maxvm`, updating ONLY the torsion fields in each
+3_fea_fields/<case>.npz in place. Vertical/horizontal/diagonal loads and all
+geometry are left untouched.
+
+Usage:
+    python apply_torsion_patch.py <your 3_fea_fields dir> <extracted patch dir>
+
+Also update deepjebpp_labels.csv and metadata.json from the v1.1 release
+(these small files are included in the release directly).
+"""
+import numpy as np, sys, os, glob
+if len(sys.argv) != 3:
+    print(__doc__); sys.exit(1)
+fields_dir, patch_dir = sys.argv[1], sys.argv[2]
+TOR_KEYS = ['tor_U', 'tor_vm', 'tor_maxu', 'tor_p95vm', 'tor_maxvm']
+patches = sorted(glob.glob(os.path.join(patch_dir, '*.npz')))
+n_ok = n_missing = n_mismatch = 0
+for pf in patches:
+    case = os.path.basename(pf)
+    ff = os.path.join(fields_dir, case)
+    if not os.path.exists(ff):
+        n_missing += 1; continue
+    old = dict(np.load(ff)); pat = np.load(pf)
+    if 'surface_points' in old and 'surface_points' in pat.files \
+       and not np.array_equal(old['surface_points'], pat['surface_points']):
+        print('SKIP surface mismatch:', case); n_mismatch += 1; continue
+    for k in TOR_KEYS:
+        if k in pat.files:
+            old[k] = pat[k]
+    tmp = ff + '.tmp'
+    np.savez_compressed(tmp, **old); os.replace(tmp, ff)
+    n_ok += 1
+print(f'patched={n_ok}  missing_in_your_dir={n_missing}  surface_mismatch={n_mismatch}')
+print('Done. Torsion labels are now Z-axis (SimJEB-matching). Also replace '
+      'deepjebpp_labels.csv and metadata.json with the v1.1 copies.')

metadata.json

@@ -2,7 +2,7 @@
   "license": "ODC-By v1.0",
   "license_url": "https://opendatacommons.org/licenses/by/1-0/",
   "dataset": "DeepJEB++ TRELLIS-generated jet-engine bracket surface-FEA dataset",
-  "version": "1.0",
+  "version": "1.1",
   "date": "2026-06-07",
   "n_cases": 15360,
   "case_id_format": "<base>-<sub>-diag_xz_mm_IS<aug>  (shared by mesh / field / BC / label row)",
@@ -61,8 +61,8 @@
       "type": "moment",
       "direction": [
         0.0,
-        1.0,
-        0.0
+        0.0,
+        1.0
       ],
       "magnitude": 565000.0,
       "unit": "N*mm"
@@ -110,6 +110,9 @@
   },
   "notes": {
     "mass_vs_cad_validation": "The paper's 'R^2=0.9999 vs CAD' mass validation refers to the volume->mass pipeline checked against CAD reference masses (SimJEB GT, which has CAD). Generated TRELLIS brackets have no CAD counterpart, so that validation is not reproduced per-case here; the same deterministic volume pipeline is used.",
-    "all_at_25k": "All 15,360 field/BC entries are at the same 25k FEM mesh density (consistent labels)."
-  }
+    "all_at_25k": "All 15,360 field/BC entries are at the same 25k FEM mesh density (consistent labels).",
+    "torsion_axis_fix": "torsion moment axis corrected Y->Z (2026-07-19)",
+    "changelog": "v1.1 (2026-07-19): torsion moment axis corrected Y->Z to match SimJEB reference torsion condition; tor_maxvm added to labels. v1.0 (2026-06-07): initial release (torsion moment about Y-axis, retained via git tag)."
+  },
+  "revision_date": "2026-07-19"
 }