Upload folder using huggingface_hub

.gitattributes

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README.md

@@ -0,0 +1,69 @@
+---
+license: other
+#User-Defined Tags
+tags:
+  - Operator Learning
+  - Neural PDE Solvers
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">PDENNEval</span>
+  </strong>
+</p>
+
+## Dataset Description
+
+PDENNEval is a comprehensive dataset for evaluating neural-network-based PDE solving methods, introduced in an IJCAI 2024 paper. It covers function learning and operator learning tasks and includes 15 types of PDE problems across multiple scientific domains, including fluids, materials, finance, and electromagnetics.
+
+The dataset consists of 10 PDEBench data files and 6 self-generated data files, totaling approximately 286.9 GB. It can be used for model sanity checks, training, evaluation, and comparisons across problems.
+
+Paper: [PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://www.ijcai.org/proceedings/2024/573)
+
+## Supported Tasks
+
+| Scenario | Description |
+|---|---|
+| PDE solution field prediction | Predict PDE solutions from equation parameters, coordinates, or initial conditions. |
+| Function learning evaluation | Compare neural networks' ability to solve individual PDE problems. |
+| Operator learning evaluation | Evaluate model generalization across different input conditions and equation tasks. |
+| Cross-domain scientific computing | Cover fluid, diffusion-reaction, materials, finance, and electromagnetics problems. |
+
+## Dataset Format and Structure
+
+All data files are stored in HDF5 format with the `.hdf5` or `.h5` extension:
+
+```text
+data/
+  PDEBench data files
+  Self-generated PDE data files
+```
+
+The data includes coordinates, time, coefficient fields, boundary conditions, and solution fields for one-, two-, and three-dimensional PDEs. It covers problems such as Advection, Burgers, Diffusion-Reaction, Darcy Flow, Shallow Water, Allen-Cahn, Cahn-Hilliard, Navier-Stokes, Euler, Maxwell, and Black-Scholes-Barenblatt.
+
+Fields and tensor shapes vary across PDEs. Refer to the data files and `metadata/pdenneval_schema.json` for the specific schema.
+
+## How to Use the Dataset
+
+This dataset is designed for the `OneScience-Sugon/PDENNEval` model. Download the dataset and model:
+
+```bash
+hf download --dataset OneScience-Sugon/pdenneval --local-dir ./pdenneval
+hf download --model OneScience-Sugon/PDENNEval --local-dir ./PDENNEval
+```
+
+## Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## Citation and License
+
+- Original PDENNEval paper: [PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://doi.org/10.24963/ijcai.2024/573)
+- Original PDEBench paper: [PDEBench: An Extensive Benchmark for Scientific Machine Learning](https://arxiv.org/abs/2210.07182)
+- PDEBench dataset: [PDEBench Datasets](https://doi.org/10.18419/darus-2986)
+- This repository retains source attribution. Before public distribution or republication, confirm the licensing requirements with the upstream projects.

README_zh.md

@@ -0,0 +1,69 @@
+---
+license: other
+#用户自定义标签
+tags:
+  - Operator Learning
+  - Neural PDE Solvers
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">PDENNEval</span>
+  </strong>
+</p>
+
+## 数据集描述
+
+PDENNEval 是用于评测神经网络 PDE 求解方法的综合数据集，来源于 IJCAI 2024 论文。数据覆盖函数学习和算子学习任务，包含流体、材料、金融和电磁等多个科学领域的 15 类 PDE 问题。
+
+数据集由 10 个 PDEBench 数据文件和 6 个自生成数据文件组成，总规模约 286.9 GB，可用于模型预检、训练、评测和跨问题比较。
+
+论文：[PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://www.ijcai.org/proceedings/2024/573)
+
+## 数据集支持的任务
+
+| 场景 | 说明 |
+|---|---|
+| PDE 解场预测 | 根据方程参数、坐标或初始条件预测 PDE 解。 |
+| 函数学习评测 | 比较神经网络对单个 PDE 问题的求解能力。 |
+| 算子学习评测 | 评估模型在不同输入条件和方程任务上的泛化能力。 |
+| 跨领域科学计算 | 覆盖流体、扩散反应、材料、金融和电磁问题。 |
+
+## 数据集的格式和结构
+
+数据文件统一采用 HDF5 格式，扩展名为 `.hdf5` 或 `.h5`：
+
+```text
+data/
+  PDEBench 数据文件
+  自生成 PDE 数据文件
+```
+
+数据内容包括一维、二维和三维 PDE 的坐标、时间、系数场、边界条件以及解场。覆盖 Advection、Burgers、Diffusion-Reaction、Darcy Flow、Shallow Water、Allen-Cahn、Cahn-Hilliard、Navier-Stokes、Euler、Maxwell 和 Black-Scholes-Barenblatt 等问题。
+
+不同 PDE 的字段和张量形状存在差异，具体 schema 以数据文件及 `metadata/pdenneval_schema.json` 为准。
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/PDENNEval` 模型。下载数据集和模型：
+
+```bash
+hf download --dataset OneScience-Sugon/pdenneval --local-dir ./pdenneval
+hf download --model OneScience-Sugon/PDENNEval --local-dir ./PDENNEval
+```
+
+## OneScience 官方信息
+
+| 平台 | OneScience 主仓库 | Skills 仓库 |
+|---|---|---|
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## 引用与许可证
+
+- PDENNEval 原始论文：[PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://doi.org/10.24963/ijcai.2024/573)
+- PDEBench 原始论文：[PDEBench: An Extensive Benchmark for Scientific Machine Learning](https://arxiv.org/abs/2210.07182)
+- PDEBench 数据集：[PDEBench Datasets](https://doi.org/10.18419/darus-2986)
+- 本仓库保留来源说明；公开分发或二次发布前，请根据上游项目确认许可证要求。

data/generated_data/1D_Allen-Cahn.zip

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+size 3457976971

data/generated_data/2D_Allen-Cahn.zip

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+size 6107195956

data/generated_data/3D_Euler.zip

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+size 54185689088

data/generated_data/3D_Maxwell.zip

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data/generated_data/Black-Scholes-Barenblatt.zip

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data/generated_data/Cahn-Hilliard.zip

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+version https://git-lfs.github.com/spec/v1
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+size 1761083392

data/generated_data/downlaod_pdenneval.sh

@@ -0,0 +1,60 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+OUTDIR="${1:-./PDENNEval_AI4SC}"
+USE_IP="${USE_IP:-0}"
+
+mkdir -p "$OUTDIR"
+
+# If DNS is unstable, run with: USE_IP=1 bash download_pdenneval_aisccc.sh
+if [ "$USE_IP" = "1" ]; then
+  BASE="http://121.14.195.11"
+  WGET_HOST_ARG='--header=Host: file.aisccc.cn'
+else
+  BASE="http://file.aisccc.cn"
+  WGET_HOST_ARG=''
+fi
+
+download_one() {
+  local name="$1"
+  local path="$2"
+  local url="${BASE}${path}"
+
+  echo
+  echo "[DOWNLOAD] ${name}"
+  echo "URL: ${url}"
+
+  # shellcheck disable=SC2086
+  wget -c \
+    --tries=0 \
+    --timeout=30 \
+    --read-timeout=60 \
+    --waitretry=10 \
+    $WGET_HOST_ARG \
+    -O "${OUTDIR}/${name}" \
+    "${url}"
+}
+
+echo "Download directory: ${OUTDIR}"
+echo "Estimated verified ZIP total size: about 75.5 GB"
+
+download_one "1D_Allen-Cahn.zip" \
+  "/disk_attach/4195cff84ba4ada4b07ee46880715c67.zip"
+
+download_one "Cahn-Hilliard.zip" \
+  "/disk_attach/c0f259d64826755880ff632678d32796.zip"
+
+download_one "2D_Allen-Cahn.zip" \
+  "/disk_attach/2e9ebaf28feae9ba98e1f881964a2364.zip"
+
+download_one "Black-Scholes-Barenblatt.zip" \
+  "/disk_attach/9a14b5efa874245ecf26233c1eb05fa7.zip"
+
+download_one "3D_Euler.zip" \
+  "/disk_attach/b11a721ab909c335a133cb38d3b6c942.zip"
+
+download_one "3D_Maxwell.zip" \
+  "/disk_attach/6bd1b7871a691a330c60acf4a83bc7dd.zip"
+
+echo
+echo "All downloads finished."

data/pdebench_data/1D_Advection_Sols_beta0.1.hdf5

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+size 8232966952

data/pdebench_data/1D_Advection_Sols_beta1.0.hdf5

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+size 4136968376

data/pdebench_data/1D_Burgers_Sols_Nu0.001.hdf5

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data/pdebench_data/1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5

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data/pdebench_data/1D_diff-sorp_NA_NA.h5

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data/pdebench_data/2D_CFD_Rand_M0.1_Eta0.1_Zeta0.1_periodic_128_Train.hdf5

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data/pdebench_data/2D_DarcyFlow_beta0.1_Train.hdf5

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+size 1310724488

data/pdebench_data/2D_rdb_NA_NA.h5

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data/pdebench_data/3D_CFD_Rand_M1.0_Eta1e-08_Zeta1e-08_periodic_Train.hdf5

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+size 88080391768

data/pdebench_data/ReacDiff_Nu0.5_Rho1.0.hdf5

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data/pdebench_data/download_pdebench_selected.sh

@@ -0,0 +1,82 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Download selected PDEBench files from DaRUS:
+# https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986
+#
+# Usage:
+#   bash download_pdebench_selected.sh [output_dir]
+#
+# Example:
+#   bash download_pdebench_selected.sh ./pdebench_data
+
+OUT_DIR="${1:-./pdebench_data}"
+BASE_URL="https://darus.uni-stuttgart.de/api/access/datafile"
+
+mkdir -p "$OUT_DIR"
+
+if ! command -v wget >/dev/null 2>&1; then
+  echo "ERROR: wget is required but was not found." >&2
+  exit 1
+fi
+
+if ! command -v md5sum >/dev/null 2>&1; then
+  echo "ERROR: md5sum is required but was not found." >&2
+  exit 1
+fi
+
+# Format: file_id|md5|filename
+FILES=(
+  "255672|b4be2fc3383f737c76033073e6d2ccfb|1D_Advection_Sols_beta0.1.hdf5"
+  "133177|69a429239778d529cd419ed5888ea835|ReacDiff_Nu0.5_Rho1.0.hdf5"
+  "268190|44cb784d5a07aa2b1c864cabdcf625f9|1D_Burgers_Sols_Nu0.001.hdf5"
+  "133020|9d466d1213065619d087319e16d9a938|1D_diff-sorp_NA_NA.h5"
+  "164668|45655bd77d006ab539c52b7fbcf099b9|1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5"
+  "164688|b2733888745a1d64e36df813e979910b|2D_CFD_Rand_M0.1_Eta0.1_Zeta0.1_periodic_128_Train.hdf5"
+  "133219|81694ed31306ff2e5f6b76349b0b4389|2D_DarcyFlow_beta1.0_Train.hdf5"
+  "133021|75d838c47aa410694bdc912ea7f22282|2D_rdb_NA_NA.h5"
+  "164693|45892a12d1066d54af74badae55c438e|3D_CFD_Rand_M1.0_Eta1e-08_Zeta1e-08_periodic_Train.hdf5"
+)
+
+check_md5() {
+  local expected="$1"
+  local file="$2"
+  local actual
+
+  actual="$(md5sum "$file" | awk '{print $1}')"
+  [[ "$actual" == "$expected" ]]
+}
+
+for entry in "${FILES[@]}"; do
+  IFS="|" read -r file_id md5 filename <<<"$entry"
+  url="${BASE_URL}/${file_id}"
+  dest="${OUT_DIR}/${filename}"
+
+  echo "==> ${filename}"
+
+  if [[ -f "$dest" ]] && check_md5 "$md5" "$dest"; then
+    echo "    already downloaded and MD5 OK, skipping."
+    continue
+  fi
+
+  wget \
+    --continue \
+    --tries=0 \
+    --waitretry=10 \
+    --timeout=60 \
+    --read-timeout=60 \
+    --show-progress \
+    --output-document="$dest" \
+    "$url"
+
+  if check_md5 "$md5" "$dest"; then
+    echo "    MD5 OK."
+  else
+    echo "ERROR: MD5 check failed for ${dest}" >&2
+    echo "       Expected: ${md5}" >&2
+    echo "       Actual:   $(md5sum "$dest" | awk '{print $1}')" >&2
+    exit 1
+  fi
+done
+
+echo "All selected PDEBench files downloaded successfully."

data/pdebench_data/wget-log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48a8464f675a383d8e8a35e16b2ccfc53c31bd1e053bd0ef9f1949af8ceadb2b
+size 12955840

data_integrity_summary.json

@@ -0,0 +1,41 @@
+{
+  "all_match": true,
+  "files": [
+    {
+      "source_path": "cfd_dataset/PDENNEval/data/1D_Advection_Sols_beta1.0.hdf5",
+      "standardized_path": "modelscope_standardized/dataset/cfd_pdenneval/data/1D_Advection_Sols_beta1.0.hdf5",
+      "source_name": "1D_Advection_Sols_beta1.0.hdf5",
+      "standardized_name": "1D_Advection_Sols_beta1.0.hdf5",
+      "same_filename": true,
+      "size": 4136968376,
+      "source_sha256": "0ccd649b1d5ecca8a5ae417a506dec5ef57ea365f7bce46afa190d34fe02dcc7",
+      "standardized_sha256": "0ccd649b1d5ecca8a5ae417a506dec5ef57ea365f7bce46afa190d34fe02dcc7",
+      "content_match": true,
+      "note": null
+    },
+    {
+      "source_path": "cfd_dataset/PDENNEval/data/268190",
+      "standardized_path": "modelscope_standardized/dataset/cfd_pdenneval/data/1D_Burgers_Sols_Nu0.001.hdf5",
+      "source_name": "268190",
+      "standardized_name": "1D_Burgers_Sols_Nu0.001.hdf5",
+      "same_filename": false,
+      "size": 8232968312,
+      "source_sha256": "afeeed1c40ce01d2ba5e1702f4f66b6bf95c65943eb0e4847691e9a10d1cb50d",
+      "standardized_sha256": "afeeed1c40ce01d2ba5e1702f4f66b6bf95c65943eb0e4847691e9a10d1cb50d",
+      "content_match": true,
+      "note": "268190 was renamed to the HDF5 filename shown in wget-log"
+    },
+    {
+      "source_path": "cfd_dataset/PDENNEval/data/2D_DarcyFlow_beta0.1_Train.hdf5",
+      "standardized_path": "modelscope_standardized/dataset/cfd_pdenneval/data/2D_DarcyFlow_beta0.1_Train.hdf5",
+      "source_name": "2D_DarcyFlow_beta0.1_Train.hdf5",
+      "standardized_name": "2D_DarcyFlow_beta0.1_Train.hdf5",
+      "same_filename": true,
+      "size": 1310724488,
+      "source_sha256": "74bbdf8b5db9cfd36168b003114673261337fd0422fa492ab1cb0c79f5d157e8",
+      "standardized_sha256": "74bbdf8b5db9cfd36168b003114673261337fd0422fa492ab1cb0c79f5d157e8",
+      "content_match": true,
+      "note": null
+    }
+  ]
+}

files_sha256.jsonl

@@ -0,0 +1,3 @@
+{"path": "data/1D_Advection_Sols_beta1.0.hdf5", "size": 4136968376, "sha256": "0ccd649b1d5ecca8a5ae417a506dec5ef57ea365f7bce46afa190d34fe02dcc7"}
+{"path": "data/1D_Burgers_Sols_Nu0.001.hdf5", "size": 8232968312, "sha256": "afeeed1c40ce01d2ba5e1702f4f66b6bf95c65943eb0e4847691e9a10d1cb50d"}
+{"path": "data/2D_DarcyFlow_beta0.1_Train.hdf5", "size": 1310724488, "sha256": "74bbdf8b5db9cfd36168b003114673261337fd0422fa492ab1cb0c79f5d157e8"}

metadata/pdenneval_schema.json

@@ -0,0 +1,42 @@
+{
+  "dataset": "PDENNEval",
+  "format": "HDF5",
+  "files": [
+    {
+      "path": "data/2D_DarcyFlow_beta0.1_Train.hdf5",
+      "pde": "2D Darcy Flow",
+      "attrs": {"beta": 0.1},
+      "datasets": {
+        "nu": {"shape": [10000, 128, 128], "dtype": "float32"},
+        "tensor": {"shape": [10000, 1, 128, 128], "dtype": "float32"},
+        "x-coordinate": {"shape": [128], "dtype": "float32"},
+        "y-coordinate": {"shape": [128], "dtype": "float32"}
+      },
+      "standard_model_configs": ["conf/fno_2d_darcy.yaml"]
+    },
+    {
+      "path": "data/1D_Burgers_Sols_Nu0.001.hdf5",
+      "pde": "1D Burgers",
+      "source_note": "原始数据目录中文件名为 268190，wget-log 中的远端 Content-Disposition 文件名为 1D_Burgers_Sols_Nu0.001.hdf5；标准包中按真实数据名重命名。",
+      "attrs": {"Nu": 0.001},
+      "datasets": {
+        "tensor": {"shape": [10000, 201, 1024], "dtype": "float32"},
+        "x-coordinate": {"shape": [1024], "dtype": "float32"},
+        "t-coordinate": {"shape": [202], "dtype": "float32"}
+      },
+      "standard_model_configs": ["conf/fno_1d_burgers.yaml"]
+    },
+    {
+      "path": "data/1D_Advection_Sols_beta1.0.hdf5",
+      "pde": "1D Advection",
+      "source_note": "当前文件可读且随数据集上传；原始模型目录中多数 Advection 配置引用 beta0.1 文件，只有 MPNN 配置引用 beta1.0 但其 PDE 元数据仍需下游确认，因此未作为默认运行场景。",
+      "attrs": {"Nu": 0.5, "rho": 1.0},
+      "datasets": {
+        "tensor": {"shape": [10000, 101, 1024], "dtype": "float32"},
+        "x-coordinate": {"shape": [1024], "dtype": "float32"},
+        "t-coordinate": {"shape": [102], "dtype": "float32"}
+      },
+      "standard_model_configs": []
+    }
+  ]
+}

onescience_relations.yaml

@@ -0,0 +1,16 @@
+required_datasets: []
+optional_datasets: []
+compatible_models:
+  - id: OneScience/PDENNEval
+    role: train_data
+    required_for: [preflight, train, evaluate]
+    resource_ref:
+      platform: modelscope
+      repo_id: OneScience/PDENNEval
+      repo_type: model
+      url: https://modelscope.cn/models/OneScience/PDENNEval
+      revision: main
+      readme_path: README.md
+      manifest_path: onescience_run_manifest.yaml
+    expected_local_path: session_workdir/data
+    expected_env: ONESCIENCE_PDENNEVAL_DATA_DIR

onescience_run_manifest.yaml

@@ -0,0 +1,312 @@
+onescience_manifest_version: "0.1"
+resource_type: dataset
+
+resource:
+  id: OneScience/pdenneval
+  name: pdenneval
+  domain: cfd
+  domain_tags: [cfd, pde, pdebench, hdf5]
+  task: pdenneval_train_eval_dataset
+  task_tags: [train_data, eval_data, dataset_validation]
+  modalities: [gridded_field, pde_solution]
+  input_formats: [hdf5]
+  output_formats: [hdf5]
+  summary: PDENNEval 配套 HDF5 数据集，包含 2D Darcy Flow、1D Burgers 和 1D Advection 数据文件。
+
+platform_resource:
+  primary:
+    platform: modelscope
+    repo_id: OneScience/pdenneval
+    repo_type: dataset
+    url: https://modelscope.cn/datasets/OneScience/pdenneval
+    revision: main
+    readme_path: README.md
+    manifest_path: onescience_run_manifest.yaml
+  mirrors: []
+  access:
+    visibility: public
+    license: unknown
+
+website_integration:
+  enabled: true
+  click_target:
+    platform: modelscope
+    resource_url: https://modelscope.cn/datasets/OneScience/pdenneval
+  llm_handoff:
+    readme_required: true
+    manifest_required: true
+    download_readme_first: true
+    resolve_related_models: true
+    default_run_goal: dataset_validation
+    cwd_note: 如果使用 modelscope download --cache_dir 下载数据集，请先 cd 到实际下载后的数据集仓库根目录；模型侧将 ONESCIENCE_PDENNEVAL_DATA_DIR 指向本仓库 data 目录。
+
+runtime:
+  enabled: true
+  onescience_domain: cfd
+  min_onescience_version: null
+  supported_execution: [local_dataset_validation, model_training_input, model_evaluation_input]
+  environment:
+    exported_env:
+      ONESCIENCE_PDENNEVAL_DATA_DIR: <dataset_repo_root>/data
+  dependencies:
+    python: ">=3.10"
+    python_packages: [h5py, numpy, pyyaml]
+
+onescience:
+  repo: https://gitee.com/onescience-ai/onescience
+  official_links:
+    gitee:
+      doc: https://gitee.com/onescience-ai/onescience-doc
+      onescience: https://gitee.com/onescience-ai/onescience
+      skills: https://gitee.com/onescience-ai/oneskills
+    github:
+      doc: https://github.com/onescience-ai/OneScience-doc
+      onescience: https://github.com/onescience-ai/OneScience
+      skills: https://github.com/onescience-ai/oneskills
+  install:
+    required_by_default: false
+    command: bash install.sh cfd
+  source_paths:
+    - onescience/src/onescience/datapipes/cfd/PDENNEval.py
+  compatibility:
+    examples_path: onescience/examples/cfd/PDENNEval
+    status: examples_compatible
+  datapipe: onescience.datapipes.cfd.PDENNEval
+
+runtime_package:
+  kind: standard_runtime_package
+  package_root: .
+  standard_layout:
+    workdir: .
+    data_dir: data
+    metadata_dir: metadata
+    output_dir: validation_outputs
+  apply_policy:
+    mode: direct_use
+    target: session_workdir
+    overwrite: false
+    protect_installed_onescience: true
+  entry_files:
+    - data/2D_DarcyFlow_beta0.1_Train.hdf5
+    - data/1D_Burgers_Sols_Nu0.001.hdf5
+    - metadata/pdenneval_schema.json
+    - scripts/validate_pdenneval_dataset.py
+    - files_sha256.jsonl
+  entrypoints:
+    preflight: scripts/validate_pdenneval_dataset.py
+    validate: scripts/validate_pdenneval_dataset.py
+    inference: null
+    train: null
+    finetune: null
+    evaluate: null
+    visualize: null
+    deploy: null
+
+files:
+  model_files: []
+  dataset_files:
+    - path: data/2D_DarcyFlow_beta0.1_Train.hdf5
+      role: train_eval_data
+      description_zh: 2D Darcy Flow HDF5 数据，包含 nu 系数场、tensor 解场和 x/y 坐标。
+      required: true
+      required_for: [dataset_validation, train, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data/2D_DarcyFlow_beta0.1_Train.hdf5
+      sha256: 74bbdf8b5db9cfd36168b003114673261337fd0422fa492ab1cb0c79f5d157e8
+      size: 1310724488
+    - path: data/1D_Burgers_Sols_Nu0.001.hdf5
+      role: train_eval_data
+      description_zh: 1D Burgers HDF5 数据，包含 tensor 解场、x 坐标和 t 坐标。
+      required: true
+      required_for: [dataset_validation, train, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data/1D_Burgers_Sols_Nu0.001.hdf5
+      sha256: afeeed1c40ce01d2ba5e1702f4f66b6bf95c65943eb0e4847691e9a10d1cb50d
+      size: 8232968312
+    - path: data/1D_Advection_Sols_beta1.0.hdf5
+      role: supplemental_data
+      description_zh: 1D Advection 可读 HDF5 数据，当前不作为默认模型运行场景。
+      required: false
+      required_for: [research_reference]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data/1D_Advection_Sols_beta1.0.hdf5
+      sha256: 0ccd649b1d5ecca8a5ae417a506dec5ef57ea365f7bce46afa190d34fe02dcc7
+      size: 4136968376
+    - path: files_sha256.jsonl
+      role: file_size_sha256_inventory
+      description_zh: 标准化数据文件的大小和 SHA256 清单，用于完整性校验。
+      required: true
+      required_for: [dataset_validation]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: files_sha256.jsonl
+    - path: data_integrity_summary.json
+      role: source_to_standardized_integrity_summary
+      description_zh: 原始数据与标准化数据的文件名、大小、SHA256 对照摘要。
+      required: true
+      required_for: [dataset_validation]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data_integrity_summary.json
+  config_files:
+    - path: metadata/pdenneval_schema.json
+      role: dataset_schema
+      description_zh: 描述 PDENNEval HDF5 文件、字段、shape、dtype 和默认模型配置关系。
+      required: true
+      required_for: [dataset_validation, train, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: metadata/pdenneval_schema.json
+  sample_files:
+    - path: scripts/validate_pdenneval_dataset.py
+      role: dataset_validation_entry
+      description_zh: 数据集读取验证脚本，检查 HDF5 schema、可读性、文件大小和可选 SHA256。
+      required: true
+      required_for: [dataset_validation]
+      source: {platform: modelscope, repo_id: OneScience/pdenneval, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: scripts/validate_pdenneval_dataset.py
+
+dataset:
+  format: [hdf5]
+  sample_unit: 一个 PDE case 的时空网格解场或系数场。
+  schema:
+    path: metadata/pdenneval_schema.json
+  splits:
+    train: 由模型 datapipe 按配置内部切分。
+    validation: 由模型 datapipe 按配置内部切分。
+    test: null
+
+relations:
+  required_datasets: []
+  optional_datasets: []
+  compatible_models:
+    - id: OneScience/PDENNEval
+      role: train_data
+      required_for: [preflight, train, evaluate]
+      resource_ref:
+        platform: modelscope
+        repo_id: OneScience/PDENNEval
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/PDENNEval
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+      expected_local_path: session_workdir/data
+      expected_env: ONESCIENCE_PDENNEVAL_DATA_DIR
+
+run_matrix:
+  scenarios:
+    - name: validate_hdf5_schema
+      capability: dataset_validation
+      default: true
+      required_datasets:
+        - id: OneScience/pdenneval
+          role: train_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files:
+        - data/2D_DarcyFlow_beta0.1_Train.hdf5
+        - data/1D_Burgers_Sols_Nu0.001.hdf5
+        - metadata/pdenneval_schema.json
+      preconditions: [当前工作目录为数据集仓库根目录。]
+      command_refs: [commands.preflight.validate_dataset]
+      outputs:
+        - "控制台输出 [OK] dataset validation completed"
+    - name: validate_integrity_full_hash
+      capability: dataset_validation
+      default: false
+      required_datasets:
+        - id: OneScience/pdenneval
+          role: train_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files: [files_sha256.jsonl, data/*.hdf5]
+      preconditions: [当前工作目录为数据集仓库根目录。]
+      command_refs: [commands.preflight.validate_dataset_full_hash]
+      outputs:
+        - "控制台输出 checksum manifest verified in size+sha256 mode"
+    - name: provide_to_pdenneval_model
+      capability: train
+      default: true
+      required_datasets:
+        - id: OneScience/pdenneval
+          role: train_data
+          local_path: data
+      required_model_files: [OneScience/PDENNEval:conf/fno_2d_darcy.yaml]
+      required_dataset_files:
+        - data/2D_DarcyFlow_beta0.1_Train.hdf5
+        - data/1D_Burgers_Sols_Nu0.001.hdf5
+      preconditions:
+        - 模型仓库 OneScience/PDENNEval 已下载。
+        - 设置 ONESCIENCE_PDENNEVAL_DATA_DIR 为本仓库 data 目录。
+      command_refs: [commands.prepare.export_dataset_env]
+      outputs:
+        - 模型侧 scripts/preflight_check.py 可以读取数据集。
+
+capabilities:
+  dataset_validation: true
+  train_input: true
+  evaluation_input: true
+  inference_input: false
+  preflight: true
+  inference: false
+  train: false
+  finetune: false
+  evaluate: false
+  visualize: false
+  deploy: false
+
+commands:
+  download:
+    - name: download_dataset
+      command: modelscope download --dataset OneScience/pdenneval
+      cwd: session_workdir
+    - name: download_model
+      command: modelscope download --model OneScience/PDENNEval
+      cwd: session_workdir
+  prepare:
+    - name: export_dataset_env
+      command: export ONESCIENCE_PDENNEVAL_DATA_DIR=/path/to/OneScience/pdenneval/data
+      cwd: .
+  preflight:
+    - name: validate_dataset
+      command: python scripts/validate_pdenneval_dataset.py
+      cwd: .
+    - name: validate_dataset_full_hash
+      command: python scripts/validate_pdenneval_dataset.py --full-hash
+      cwd: .
+  inference: []
+  train: []
+  finetune: []
+  evaluate: []
+  visualize: []
+  deploy: []
+
+expected_outputs:
+  - path: validation_outputs/
+    type: directory
+    description_zh: 预留的数据读取验证输出目录；默认验证命令主要输出控制台 OK/FAIL。
+
+diagnostics:
+  - name: missing_hdf5
+    match: "missing expected HDF5 file"
+    severity: error
+    fix_zh: 确认已完整下载 OneScience/pdenneval，并在数据集仓库根目录执行验证脚本。
+  - name: hdf5_schema_mismatch
+    match: "missing dataset"
+    severity: error
+    fix_zh: 数据文件结构与 Manifest 不一致，重新下载或检查文件是否被替换。
+  - name: checksum_mismatch
+    match: "sha256 mismatch"
+    severity: error
+    fix_zh: 文件内容与上传清单不一致，重新下载数据集。
+  - name: dependency_missing
+    match: "ModuleNotFoundError"
+    severity: error
+    fix_zh: 安装 h5py、numpy 和 PyYAML。
+
+domain_extension:
+  cfd:
+    equation_families: [darcy_flow, burgers, advection]
+    benchmark_family: PDEBench
+    data_format: HDF5
+    default_model: OneScience/PDENNEval
+    notes:
+      - 1D_Advection_Sols_beta1.0.hdf5 随包上传但不作为默认运行场景。

scripts/validate_pdenneval_dataset.py

@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+"""Validate the standardized PDENNEval dataset package."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import sys
+from pathlib import Path
+
+import h5py
+import numpy as np
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+DATA_ROOT = REPO_ROOT / "data"
+CHECKSUM_PATH = REPO_ROOT / "files_sha256.jsonl"
+
+EXPECTED_FILES = {
+    "1D_Burgers_Sols_Nu0.001.hdf5": {
+        "datasets": {
+            "tensor": {"ndim": 3, "dtype": "float32"},
+            "x-coordinate": {"ndim": 1, "dtype": "float32"},
+            "t-coordinate": {"ndim": 1, "dtype": "float32"},
+        },
+        "attrs": {"Nu": 0.001},
+    },
+    "2D_DarcyFlow_beta0.1_Train.hdf5": {
+        "datasets": {
+            "tensor": {"ndim": 4, "dtype": "float32"},
+            "nu": {"ndim": 3, "dtype": "float32"},
+            "x-coordinate": {"ndim": 1, "dtype": "float32"},
+            "y-coordinate": {"ndim": 1, "dtype": "float32"},
+        },
+        "attrs": {"beta": 0.1},
+    },
+    "1D_Advection_Sols_beta1.0.hdf5": {
+        "datasets": {
+            "tensor": {"ndim": 3, "dtype": "float32"},
+            "x-coordinate": {"ndim": 1, "dtype": "float32"},
+            "t-coordinate": {"ndim": 1, "dtype": "float32"},
+        },
+        "attrs": {},
+    },
+}
+
+
+def fail(message: str) -> None:
+    print(f"[FAIL] {message}")
+    raise SystemExit(1)
+
+
+def ok(message: str) -> None:
+    print(f"[OK] {message}")
+
+
+def warn(message: str) -> None:
+    print(f"[WARN] {message}")
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def as_float(value: object) -> float | None:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def validate_hdf5_file(path: Path, spec: dict[str, object]) -> None:
+    if not path.is_file():
+        fail(f"missing expected HDF5 file: {path}")
+    with h5py.File(path, "r") as handle:
+        for attr_name, expected in spec.get("attrs", {}).items():
+            actual = as_float(handle.attrs.get(attr_name))
+            if actual is None or not math.isclose(actual, float(expected), rel_tol=1e-6, abs_tol=1e-12):
+                fail(f"{path.name} attr {attr_name!r} expected {expected}, got {handle.attrs.get(attr_name)!r}")
+        for dataset_name, dataset_spec in spec["datasets"].items():
+            if dataset_name not in handle:
+                fail(f"{path.name} missing dataset {dataset_name!r}")
+            dataset = handle[dataset_name]
+            if dataset.ndim != dataset_spec["ndim"]:
+                fail(f"{path.name}/{dataset_name} ndim expected {dataset_spec['ndim']}, got {dataset.ndim}")
+            if str(dataset.dtype) != dataset_spec["dtype"]:
+                fail(f"{path.name}/{dataset_name} dtype expected {dataset_spec['dtype']}, got {dataset.dtype}")
+            if any(dim <= 0 for dim in dataset.shape):
+                fail(f"{path.name}/{dataset_name} has invalid shape {dataset.shape}")
+            probe = np.asarray(dataset[0])
+            if not np.isfinite(probe).all():
+                fail(f"{path.name}/{dataset_name} first slice contains non-finite values")
+        ok(f"{path.name} HDF5 schema is readable")
+
+
+def verify_checksums(full_hash: bool) -> None:
+    if not CHECKSUM_PATH.exists():
+        warn(f"checksum manifest is not present: {CHECKSUM_PATH}")
+        return
+    records = [json.loads(line) for line in CHECKSUM_PATH.read_text(encoding="utf-8").splitlines() if line.strip()]
+    if not records:
+        fail("checksum manifest is empty")
+    for record in records:
+        path = REPO_ROOT / record["path"]
+        if not path.is_file():
+            fail(f"checksum entry points to missing file: {path}")
+        size = path.stat().st_size
+        if size != record["size"]:
+            fail(f"size mismatch for {path}: expected {record['size']}, got {size}")
+        if full_hash:
+            digest = sha256_file(path)
+            if digest != record["sha256"]:
+                fail(f"sha256 mismatch for {path}")
+    mode = "size+sha256" if full_hash else "size"
+    ok(f"checksum manifest verified in {mode} mode: {len(records)} files")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--full-hash", action="store_true", help="verify SHA256 for all large HDF5 files")
+    args = parser.parse_args()
+
+    if not DATA_ROOT.is_dir():
+        fail(f"dataset data root does not exist: {DATA_ROOT}")
+    for filename, spec in EXPECTED_FILES.items():
+        validate_hdf5_file(DATA_ROOT / filename, spec)
+    verify_checksums(args.full_hash)
+    ok("dataset validation completed")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())