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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+results/checkpoint_inference.png filter=lfs diff=lfs merge=lfs -text
+results/figure1.png filter=lfs diff=lfs merge=lfs -text
+results/training_loss.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,88 @@
+---
+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.

config.json

@@ -0,0 +1,11 @@
+{
+  "model_type": "nots_darcy_fno",
+  "architectures": ["NOTS"],
+  "framework": "pytorch",
+  "experiment": "darcy_flow_rates_16x16",
+  "input_channels": 1,
+  "output_channels": 1,
+  "resolution": [16, 16],
+  "checkpoint": "weight/best_model.pt",
+  "dataset": "OneScience/NOTS-Darcy-16"
+}

config/config.yaml

@@ -0,0 +1,60 @@
+paper:
+  title: Thompson Sampling in Function Spaces via Neural Operators
+  arxiv: '2506.21894'
+  target: Figure 1 and Table 1 Darcy negative total flow rates NOTS
+paths:
+  data_dir: data
+  output_dir: .
+  train_file: darcy_train_16.pt
+  test_file: darcy_test_16.pt
+model:
+  n_modes: [16, 16]
+  hidden_channels: 64
+  in_channels: 1
+  out_channels: 1
+  n_layers: 4
+  lifting_channel_ratio: 2
+  projection_channel_ratio: 2
+  positional_embedding: grid
+  channel_mlp_expansion: 0.5
+  channel_mlp_dropout: 0.0
+  channel_mlp_skip: soft-gating
+  fno_skip: linear
+  norm: null
+  domain_padding: null
+  factorization: null
+  fno_block_precision: full
+training:
+  epochs: 10
+  batch_size: 32
+  lr: 0.001
+  lambda_reg: 0.0001
+  optimizer: AdamW
+  weight_decay: 0.0
+  scheduler: CosineAnnealingLR
+  normalization: none
+  shuffle: false
+  precision: float32
+experiment:
+  iterations: 300
+  n_init: 1
+  seeds: [42, 43, 44, 45, 46, 47, 48, 49, 50, 51]
+  pool_size: 1000
+  resolution: 16
+  noise_variance_fraction: 0.01
+  shrink_pool: false
+  prediction_batch_size: 64
+  run_random_baseline: true
+  std_ddof: 0
+  objective_sign: appendix_D1_negative_physical_outflow
+runtime:
+  require_case_slurm_job: false
+  device: cuda
+  num_threads: 8
+reference:
+  NOTS: {mean: 0.012, std: 0.001}
+  RS: {mean: 0.872, std: 0.022}
+  BO: {mean: 0.703, std: 0.045}
+  BFO: {mean: 0.788, std: 0.066}
+  GP-TS: {mean: 0.674, std: 0.050}
+  STO-NTS: {mean: 0.068, std: 0.002}

model/NOTS.py

@@ -0,0 +1,42 @@
+"""Independent NOTS experiment model; the general FNO is neuraloperator 1.0.2.
+
+No source from the authors' NOTS repository is included here. Paper Algorithm 2
+requires a newly initialized, fully trained FNO at every posterior draw.
+"""
+import math
+
+import torch
+from torch import nn
+from neuralop.models import FNO
+
+
+class NOTS(nn.Module):
+    """Scalar permeability [B,1,H,W] -> pressure [B,1,H,W]."""
+
+    def __init__(self, **model_config):
+        super().__init__()
+        self.model_config = dict(model_config)
+        self.fno = FNO(**model_config)
+        self.reset_posterior_parameters()
+
+    @torch.no_grad()
+    def reset_posterior_parameters(self):
+        """He Gaussian weights, zero biases, including complex spectral weights.
+
+        Complex normal_ uses the requested total variance across real/imaginary
+        parts, and the regularizer uses the modulus squared of those weights.
+        """
+        for name, parameter in self.named_parameters():
+            if 'bias' in name:
+                parameter.zero_()
+            else:
+                fan_in = parameter.shape[1] * math.prod(parameter.shape[2:]) if parameter.ndim > 1 else parameter.numel()
+                parameter.normal_(0.0, math.sqrt(2.0 / max(1, fan_in)))
+
+    def forward(self, permeability):
+        if permeability.ndim != 4 or permeability.shape[1] != 1:
+            raise ValueError('NOTS expects permeability [batch,1,height,width]')
+        return self.fno(permeability)
+
+    def l2_penalty(self):
+        return sum(p.abs().square().sum() for p in self.parameters() if p.requires_grad)

model/__init__.py

@@ -0,0 +1 @@
+from .NOTS import NOTS

requirements.txt

@@ -0,0 +1,9 @@
+# Torch is supplied by the site's DTK/DCU conda environment.
+neuraloperator==1.0.2
+tensorly==0.9.0
+tensorly-torch==0.5.0
+torch-harmonics==0.7.3
+configmypy==0.2.0
+numpy>=1.25,<2
+matplotlib
+PyYAML

results/inference_metrics.json

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+{
+  "checkpoint": "weight/best_model.pt",
+  "checkpoint_sha256": "2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2",
+  "checkpoint_sha256_scope": "original trained file before portability-only config fingerprint update",
+  "selected_seed": 49,
+  "selection_train_mse": 0.00597818839958679,
+  "splits": {
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+        16
+      ],
+      "all_finite": true,
+      "data": {
+        "path": "data/darcy_test_16.pt",
+        "sha256": "d2b19af9d4a6ae04ba2de54b8ecccc8b89766472faf6ce7f506c0b2a60f69a4c",
+        "n": 50,
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+        "dtype": "float32"
+      }
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+    "train": {
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+      "mse": 0.0304754339158535,
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+      "data": {
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+        "sha256": "0be662acdb1dab7dfd1a1da0b140e97f75cc94f98a14edcf768abe6968b3b418",
+        "n": 1000,
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+      }
+    }
+  }
+}

results/inference_test.pt

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results/inference_train.pt

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results/metrics.json

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+  "metric_definition": "mean_trials(sum_t(true_max - true_selected_t)/300)",
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+  "seeds": [
+    42,
+    43,
+    44,
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+  },
+  "unrerun_baselines": [
+    "BO",
+    "BFO",
+    "GP-TS",
+    "STO-NTS"
+  ]
+}

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results/oracle.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5771f6d177e1b798d885d5b634bba36c0511b97ecb81194de12526799fa8634
+size 6564

results/report.md

@@ -0,0 +1,17 @@
+# Darcy flow rates reproduction results
+
+NOTS measured R300/300 = 0.28903131 ± 0.016963418 (population std, ten trials).
+
+Paper Table 1 reference: 0.012 ± 0.001. This is a literature value, not measured data.
+
+All measured trajectories contain 300 queries including one random initial query. Training and acquisition use the same physical pressure scale. The paper main-text flow sign conflicts with Appendix D.1; the latter is used. Published BO/BFO/GP-TS/STO-NTS entries are reference-only, not reruns.
+
+## Verified execution
+
+Case job 975454: COMPLETED, ExitCode 0:0, elapsed 00:53:35, node a01r3n10; stderr empty. All 10 trials, 3000 oracle queries, checkpoints, strict checkpoint inference, and six PNG/PDF figures passed the completion audit. Independent CSV reaggregation agrees with metrics.json.
+
+The measured NOTS result does **not** reproduce the paper numerical value. Repairs addressed local scripts-package shadowing and neuraloperator state-dictionary metadata serialization; the prior scientific protocol was preserved.
+
+Test-set checkpoint inference: MSE 0.03060311265; mean relative L2 0.3399168253; flow-functional MAE 1.425788999. Best checkpoint: seed49, SHA256 2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2.
+
+See scheduler_status.json and completion_audit.json for actual execution evidence.

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results/table1_flow_rates.csv

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