| --- |
| license: other |
| language: |
| - en |
| tags: |
| - OneScience |
| - darcy-flow |
| - neural-operator |
| - thompson-sampling |
| --- |
| |
| # NOTS-DarcyFlowRate |
|
|
| ## Model Introduction |
|
|
| This package reproduces the Darcy flow-rates experiment in [Thompson Sampling in Function Spaces via Neural Operators](https://arxiv.org/abs/2506.21894). A neural operator predicts a 16x16 pressure field from a permeability field, then supports sequential selection using a negative total-flow objective. |
|
|
| ## Model Description |
|
|
| The run completed ten independent trials with 300 queries per trial on one DCU. The included `weight/best_model.pt` contains the selected trained model. The public checkpoint changes only the configuration fingerprint for portable data/output paths; its model parameter tensors are identical to the original run. |
|
|
| The published checkpoint SHA256 is `dac0b15574242acabd25d5bc52b7a415ec72e1d55411433402affe61876131f7`; the original training checkpoint SHA256 was `2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2`. The archived `results/inference_metrics.json` records the original file hash; rerunning inference writes a fresh metric file for the published checkpoint. |
|
|
| ## Intended Use |
|
|
| | Task | Description | |
| | --- | --- | |
| | Darcy surrogate inference | Predict pressure fields on 16x16 permeability grids | |
| | Sequential optimization | Study neural-operator Thompson sampling for flow-rate objectives | |
|
|
| ## Usage |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/NOTS-DarcyFlowRate --local-dir ./NOTS-DarcyFlowRate |
| cd NOTS-DarcyFlowRate |
| ``` |
|
|
| ### Install Dependencies |
|
|
| Use an allocated GPU/DCU compute node for the provided scripts. The tested formal run used a PyTorch DCU environment; CPU execution of the full pipeline is not claimed. |
|
|
| ```bash |
| pip install -r requirements.txt |
| pip install 'modelscope[datasets]' |
| ``` |
|
|
| ### Training Data |
|
|
| Hugging Face hosts only this model package, not a dataset mirror. Download the author's original 16x16 Darcy train/test tensors from [OneScience/NOTS-Darcy-16 on ModelScope](https://www.modelscope.cn/datasets/OneScience/NOTS-Darcy-16): |
|
|
| ```bash |
| modelscope download --dataset OneScience/NOTS-Darcy-16 --local_dir ./data |
| ``` |
|
|
| The package expects `data/darcy_train_16.pt` with 1,000 pairs and `data/darcy_test_16.pt` with 50 pairs. The original files are from the [NOTS authors' repository](https://github.com/csiro-funml/nots/tree/main/data). |
|
|
| ### Train |
|
|
| From the package root on an allocated GPU/DCU compute node: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| This runs ten configured seeds and 300 queries per seed. Save a copy of the bundled checkpoint and results before retraining. |
|
|
| ### Inference and Results |
|
|
| ```bash |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
|
|
| The reproduced final average regret was `0.289031 +/- 0.016963` across ten trials, versus `0.012 +/- 0.001` in paper Table 1. The measured test pressure-field mean relative L2 was `0.339917`. The regret is far from the paper result; this package is a documented reproduction attempt, not a paper-accuracy claim. |
|
|
| ## 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: [Thompson Sampling in Function Spaces via Neural Operators](https://arxiv.org/abs/2506.21894). |
| - Dataset: [NOTS authors' data directory](https://github.com/csiro-funml/nots/tree/main/data), originally from NeuralOperator and redistributed there under the accompanying MIT license. The data is hosted separately on ModelScope. |
|
|
