| --- |
| 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. |
|
|
| [Paper](https://doi.org/10.1016/j.asoc.2026.115899) · [Code](https://github.com/JinboYang94/HPG-Diff) |
|
|
| ## 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 |
|
|
| ```python |
| 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](https://github.com/JinboYang94/HPG-Diff). |
|
|
| ## Data Source |
|
|
| The SIMP topology examples come from [TopoDiff](https://decode.mit.edu/projects/topodiff/) by François Mazé and Faez Ahmed. HPG-Diff supplements them with displacement and principal stress line features. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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](https://creativecommons.org/licenses/by-nc/4.0/). Source materials retain their original licenses. |
|
|
