QCPINN-CavityFlow

Model Introduction

This package reproduces the unsteady lid-driven cavity experiment in QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs, 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

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:

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:

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:

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

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	OneSkills
GitHub	OneScience	OneSkills

Citation and License

• Paper: QCPINN.
• Reference field: authors' QCPINN repository. The repository has an MIT software license; the data is distributed separately with its own provenance information.