Datasets:
license: cc-by-nc-4.0
pretty_name: HPG-Diff
language:
- en
tags:
- topology-optimization
- diffusion-models
- physics-guided
- structural-design
dataset_info:
- features:
- name: image
dtype: image
- name: load
dtype:
array3_d:
shape:
- 64
- 64
- 2
dtype: float64
- name: pf
dtype:
array3_d:
shape:
- 64
- 64
- 3
dtype: float64
- name: disp
dtype:
array3_d:
shape:
- 64
- 64
- 1
dtype: float64
- name: psl
dtype: image
- name: summary
struct:
- name: y_loads
sequence: float64
- name: BC_conf
sequence:
- name: '0'
sequence: int64
- name: '1'
dtype: int64
- name: VF
dtype: float64
- name: load_nodes
sequence: float64
- name: BC_conf_x
dtype: string
- name: BC_conf_y
dtype: string
- name: load_coord
dtype:
array2_d:
shape:
- null
- 2
dtype: float64
- name: x_loads
sequence: float64
- name: id
dtype: string
splits:
- name: train
num_bytes: 7784070952
num_examples: 30000
download_size: 3647646831
dataset_size: 7784070952
config_name: train
- features:
- name: bc
dtype:
array3_d:
shape:
- 64
- 64
- 2
dtype: float32
- name: load
dtype:
array3_d:
shape:
- 64
- 64
- 2
dtype: float64
- name: pf
dtype:
array3_d:
shape:
- 64
- 64
- 3
dtype: float64
- name: disp
dtype:
array3_d:
shape:
- 64
- 64
- 1
dtype: float64
- name: psl
dtype: image
- name: compliance
sequence: float64
- name: summary
struct:
- name: y_loads
sequence: float64
- name: BC_conf
sequence:
- name: '0'
sequence: int64
- name: '1'
dtype: int64
- name: VF
dtype: float64
- name: load_nodes
sequence: float64
- name: BC_conf_x
dtype: string
- name: BC_conf_y
dtype: string
- name: load_coord
dtype:
array2_d:
shape:
- null
- 2
dtype: float64
- name: x_loads
sequence: float64
- name: id
dtype: string
splits:
- name: test_level_1
num_bytes: 581208700
num_examples: 1800
- name: test_level_2
num_bytes: 316289133
num_examples: 1000
download_size: 340573399
dataset_size: 897497833
config_name: test
configs:
- config_name: train
default: true
data_files:
- split: train
path: data/train-*
- config_name: test
data_files:
- split: test_level_1
path: data/test_level_1-*
- split: test_level_2
path: data/test_level_2-*
HPG-Diff
The dataset for HPG-Diff: Hierarchical Physics-Guided Diffusion with Differentiable Connectivity Constraints for Topology Optimization, published in Applied Soft Computing, 2026.
HPG-Diff uses displacement, principal stress lines, and strain energy density to guide topology generation. This dataset provides the structural examples, conditioning fields, and benchmark reference values used for training and evaluation.
Datasets
| Configuration | Split | Examples | Description |
|---|---|---|---|
train |
train |
30,000 | Training topologies with conditioning physics fields |
test |
test_level_1 |
1,800 | In-distribution boundary conditions |
test |
test_level_2 |
1,000 | Out-of-distribution boundary conditions |
The examples use a 64 × 64 grid. Training and evaluation have separate configurations because their fields differ.
Data Format
| Field | Shape / type | Description |
|---|---|---|
id |
string | Sample identifier |
image |
image, training only | Target topology |
load |
64 × 64 × 2 | Load field |
pf |
64 × 64 × 3 | Physical-field tensor used by the model |
disp |
64 × 64 × 1 | Displacement field |
psl |
RGB image | Principal stress line representation |
summary |
structured record | Loads, supports, coordinates, and volume fraction |
bc |
64 × 64 × 2, test only | Boundary-condition field |
compliance |
list of numbers, test only | Reference compliance values used by the evaluation code |
Usage
from datasets import load_dataset
train = load_dataset("scottyjb/HPGDiff", "train", split="train")
test_1 = load_dataset("scottyjb/HPGDiff", "test", split="test_level_1")
test_2 = load_dataset("scottyjb/HPGDiff", "test", split="test_level_2")
The training and evaluation scripts are available in the code repository.
Data Source
The SIMP topology examples come from TopoDiff by François Mazé and Faez Ahmed. HPG-Diff supplements them with displacement and principal stress line features.
Citation
@article{yang2026hpgdiff,
title = {{HPG-Diff}: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization},
author = {Yang, Jinbo and Yuan, Mingyue and Zhang, Boyuan and Kitamura, Yoshifumi and Jing, Shikai},
journal = {Applied Soft Computing},
volume = {202},
pages = {115899},
year = {2026},
doi = {10.1016/j.asoc.2026.115899}
}
The compliance field in each test row contains the complete reference vector for that split. Evaluation reads it from the first row and matches values by the original row order; keep that order when computing compliance metrics.
License
This dataset is released under CC BY-NC 4.0. Source materials retain their original licenses.