| --- |
| license: other |
| language: |
| - en |
| tags: |
| - OneScience |
| - cavity-flow |
| - physics-informed-neural-network |
| - quantum-machine-learning |
| --- |
| |
| # QCPINN-CavityFlow |
|
|
| ## Model Introduction |
|
|
| This package reproduces the unsteady lid-driven cavity experiment in [QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs](https://arxiv.org/abs/2503.16678), Table 3. The Angle-Cascade quantum-classical PINN maps `(t, x, y)` to `(u, v, p)`. Sobol-sampled PDE and boundary points drive training; the author's reference field is used for validation and evaluation, not gradient updates. |
|
|
| Ten independent runs of 20,000 epochs were completed on CPU quantum simulation. The included `weight/best_model.pt` is the validation-selected checkpoint from seed 6, epoch 1, not a claim of paper-level accuracy. Only the reference-data path in its checkpoint metadata was changed for portability; all nine parameter tensors are unchanged. The published checkpoint SHA256 is `498bcc0dbfc053ca4824b1b6cd2914e85536d0aed1e07ad50d5ef3b4a0b11b87`; the original training checkpoint SHA256 was `f24e5f3357b045ed2a49fdb8a377cd3668a5cbb8100ff3c13906e3bfce9aff11`. |
|
|
| ## Intended Use |
|
|
| | Task | Description | |
| | --- | --- | |
| | Cavity-flow field inference | Predict velocity and pressure at spatiotemporal coordinates | |
| | PINN research | Examine a quantum-classical Navier-Stokes training protocol | |
|
|
| ## Usage |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/QCPINN-CavityFlow --local-dir ./QCPINN-CavityFlow |
| cd QCPINN-CavityFlow |
| ``` |
|
|
| ### Install Dependencies |
|
|
| The formal run used Python 3.10, CPU PyTorch 2.6.0 and PennyLane 0.40.0. The `+cpu` PyTorch wheel requires the official CPU index: |
|
|
| ```bash |
| conda create -n qcpinn-cavity python=3.10 -y |
| conda activate qcpinn-cavity |
| pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu |
| pip install 'modelscope[datasets]' |
| ``` |
|
|
| ### Reference Data |
|
|
| Hugging Face hosts only the model package. Obtain the author's `cavity.mat` from [OneScience/QCPINN-Cavity on ModelScope](https://www.modelscope.cn/datasets/OneScience/QCPINN-Cavity): |
|
|
| ```bash |
| modelscope download --dataset OneScience/QCPINN-Cavity --local_dir ./data |
| ``` |
|
|
| The expected file is `data/cavity.mat`, SHA256 `4010d55dc23aa0f8ba79b24baf75e3903044bc4333d772168121cd30bc95c3ba`. It contains `cavity_internal`, `cavity_gamma0`, and `cavity_gamma1` in MATLAB v7.3/HDF5 format. No external labeled training dataset is needed. |
|
|
| ### Train |
|
|
| From a fresh package copy on a SLURM compute cluster, with the Python environment activated: |
|
|
| ```bash |
| sbatch scripts/run_all.slurm |
| ``` |
|
|
| The script runs ten seeds, 20,000 epochs per seed, then reloads checkpoints for inference and figures. Adjust the cluster's time/partition settings as needed. Preserve the bundled weight and results before retraining. |
|
|
| ### Inference and Results |
|
|
| ```bash |
| python scripts/inference.py --checkpoint weight/best_model.pt |
| python scripts/result.py |
| ``` |
|
|
| The selected checkpoint's internal-field relative L2 errors for `(u, v, p)` were `(123.70%, 121.43%, 164.19%)`. Across ten validation-best checkpoints, the means were `(200.42%, 274.73%, 148.86%)`, versus `(25.73%, 38.00%, 42.65%)` in paper Table 3. The full training was completed, but the paper accuracy was not reproduced. Historical `results/best_model/metrics.json` records the original checkpoint hash; rerunning inference records the published checkpoint hash. |
|
|
| ## OneScience Repositories |
|
|
| | Platform | Main repository | Skills repository | |
| | --- | --- | --- | |
| | Gitee | [OneScience](https://gitee.com/onescience-ai/onescience) | [OneSkills](https://gitee.com/onescience-ai/oneskills) | |
| | GitHub | [OneScience](https://github.com/onescience-ai/OneScience) | [OneSkills](https://github.com/onescience-ai/oneskills) | |
|
|
| ## Citation and License |
|
|
| - Paper: [QCPINN](https://arxiv.org/abs/2503.16678). |
| - Reference field: [authors' QCPINN repository](https://github.com/afrah/QCPINN/tree/main/data). The repository has an MIT software license; the data is distributed separately with its own provenance information. |
|
|
