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‎.github/workflows/publish.yml

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name: Publish Python package
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on:
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push:
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branches:
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- develop # Trigger the workflow on push events to the develop branch
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jobs:
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build-and-publish:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ['3.8', '3.9', '3.10'] # List of Python versions
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steps:
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- uses: actions/checkout@v3
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with:
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fetch-depth: 0
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install setuptools wheel twine build
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- name: Build package
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run: |
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python -m build
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- name: Publish package
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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twine upload dist/*

‎README.md

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@@ -27,6 +27,7 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
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| 问题类型 | 案例名称 | 优化算法 | 模型类型 | 训练方式 | 数据集 | 参考资料 |
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|-----|---------|-----|---------|----|---------|---------|
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| 相场方程 | [Allen-Cahn](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/allen_cahn) | 机理驱动 | MLP | 无监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/AllenCahn/allen_cahn.mat) | [Paper](https://arxiv.org/pdf/2402.00326) |
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| 微分方程 | [拉普拉斯方程](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/laplace2d) | 机理驱动 | MLP | 无监督学习 | - | - |
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| 微分方程 | [伯格斯方程](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/deephpms) | 机理驱动 | MLP | 无监督学习 | [Data](https://github.com/maziarraissi/DeepHPMs/tree/master/Data) | [Paper](https://arxiv.org/pdf/1801.06637.pdf) |
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| 微分方程 | [非线性偏微分方程](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/pirbn) | 机理驱动 | PIRBN | 无监督学习 | - | [Paper](https://arxiv.org/abs/2304.06234) |
@@ -38,7 +39,7 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
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| 微分方程 | [分数阶微分方程](https://github.com/PaddlePaddle/PaddleScience/blob/develop/examples/fpde/fractional_poisson_2d.py) | 机理驱动 | MLP | 无监督学习 | - | - |
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| 光孤子 | [Optical soliton](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/nlsmb) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.1007/s11071-023-08824-w)|
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| 光纤怪波 | [Optical rogue wave](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/nlsmb) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.1007/s11071-023-08824-w)|
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| 相场方程 | [Allen-Cahn](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/allen_cahn) | 机理驱动 | MLP | 无监督学习 | - | |
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| 域分解 | [XPINN](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/xpinns) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.4208/cicp.OA-2020-0164)|
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<br>
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<p align="center"><b>技术科学(AI for Technology)</b></p>
@@ -100,6 +101,7 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
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<!-- --8<-- [start:update] -->
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## 🕘最近更新
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- 添加 PirateNet(基于 Allen-cahn 方程求解) [Allen-Cahn](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/allen_cahn/)
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- 添加多目标优化算法 [Relobralo](https://paddlescience-docs.readthedocs.io/zh/latest/zh/api/loss/mtl/#ppsci.loss.mtl.Relobralo) 。
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- 添加气泡流求解案例([Bubble](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/bubble))、机翼优化案例([DeepCFD](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/deepcfd/))、热传导仿真案例([HeatPINN](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/heat_pinn))、非线性短临预报模型([Nowcasting(仅推理)](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/nowcastnet))、拓扑优化案例([TopOpt](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/topopt))、矩形平板线弹性方程求解案例([Biharmonic2D](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/biharmonic2d))。
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- 添加二维血管案例([LabelFree-DNN-Surrogate](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/labelfree_DNN_surrogate/#4))、空气激波案例([ShockWave](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/shock_wave/))、去噪网络模型([DUCNN](https://github.com/PaddlePaddle/PaddleScience/tree/develop/jointContribution/DU_CNN))、风电预测模型([Deep Spatial Temporal](https://github.com/PaddlePaddle/PaddleScience/tree/develop/jointContribution/Deep-Spatio-Temporal))、域分解模型([XPINNs](https://github.com/PaddlePaddle/PaddleScience/tree/develop/jointContribution/XPINNs))、积分方程求解案例([Volterra Equation](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/volterra_ide))、分数阶方程求解案例([Fractional Poisson 2D](https://github.com/PaddlePaddle/PaddleScience/blob/develop/examples/fpde/fractional_poisson_2d.py))。

‎deploy/python_infer/__init__.py

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from deploy.python_infer.base import Predictor
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from deploy.python_infer.pinn_predictor import PINNPredictor
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# alias as PINNPredictor can be used in most cases
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GeneralPredictor = PINNPredictor
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class GeneralPredictor(PINNPredictor):
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"""Use PINNPredictor as GeneralPredictor."""
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pass
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__all__ = [
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"Predictor",

‎deploy/python_infer/pinn_predictor.py

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Args:
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cfg (DictConfig): Running configuration.
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Examples:
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>>> import numpy as np
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>>> import paddle
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>>> from omegaconf import DictConfig
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>>> from paddle.static import InputSpec
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>>> import ppsci
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>>> from deploy.python_infer import pinn_predictor
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>>> model = ppsci.arch.MLP(("x", "y"), ("u", "v", "p"), 3, 16)
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>>> static_model = paddle.jit.to_static(
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... model,
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... input_spec=[
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... {
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... key: InputSpec([None, 1], "float32", name=key)
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... for key in model.input_keys
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... },
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... ],
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... )
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>>> paddle.jit.save(static_model, "./inference")
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>>> cfg = DictConfig(
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... {
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... "log_freq": 10,
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... "INFER": {
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... "pdmodel_path": "./inference.pdmodel",
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... "pdiparams_path": "./inference.pdiparams",
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... "device": "cpu",
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... "engine": "native",
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... "precision": "fp32",
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... "onnx_path": None,
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... "ir_optim": True,
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... "min_subgraph_size": 15,
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... "gpu_mem": 500,
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... "gpu_id": 0,
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... "max_batch_size": 10,
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... "num_cpu_threads": 10,
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... }
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... }
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... )
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>>> predictor = pinn_predictor.PINNPredictor(cfg) # doctest: +SKIP
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>>> pred = predictor.predict(
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... {
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... "x": np.random.randn(4, 1).astype("float32"),
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... "y": np.random.randn(4, 1).astype("float32"),
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... },
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... batch_size=2,
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... ) # doctest: +SKIP
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>>> for k, v in pred.items(): # doctest: +SKIP
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... print(k, v.shape) # doctest: +SKIP
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save_infer_model/scale_0.tmp_0 (4, 1)
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save_infer_model/scale_1.tmp_0 (4, 1)
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save_infer_model/scale_2.tmp_0 (4, 1)
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"""
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def __init__(

‎docs/index.md

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| 问题类型 | 案例名称 | 优化算法 | 模型类型 | 训练方式 | 数据集 | 参考资料 |
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|-----|---------|-----|---------|----|---------|---------|
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| 相场方程 | [Allen-Cahn](./zh/examples/allen_cahn.md) | 机理驱动 | MLP | 无监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/AllenCahn/allen_cahn.mat) | [Paper](https://arxiv.org/pdf/2402.00326) |
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| 微分方程 | [拉普拉斯方程](./zh/examples/laplace2d.md) | 机理驱动 | MLP | 无监督学习 | - | - |
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| 微分方程 | [伯格斯方程](./zh/examples/deephpms.md) | 机理驱动 | MLP | 无监督学习 | [Data](https://github.com/maziarraissi/DeepHPMs/tree/master/Data) | [Paper](https://arxiv.org/pdf/1801.06637.pdf) |
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| 微分方程 | [非线性偏微分方程](./zh/examples/pirbn.md) | 机理驱动 | PIRBN | 无监督学习 | - | [Paper](https://arxiv.org/abs/2304.06234) |
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| 微分方程 | [分数阶微分方程](https://github.com/PaddlePaddle/PaddleScience/blob/develop/examples/fpde/fractional_poisson_2d.py) | 机理驱动 | MLP | 无监督学习 | - | - |
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| 光孤子 | [Optical soliton](./zh/examples/nlsmb.md) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.1007/s11071-023-08824-w)|
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| 光纤怪波 | [Optical rogue wave](./zh/examples/nlsmb.md) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.1007/s11071-023-08824-w)|
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| 相场方程 | [Allen-Cahn](./zh/examples/allen_cahn.md) | 机理驱动 | MLP | 无监督学习 | - | |
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| 域分解 | [XPINN](./zh/examples/xpinns.md) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.4208/cicp.OA-2020-0164)|
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<p align="center"><b>技术科学(AI for Technology)</b></p>

‎docs/zh/api/arch.md

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- AMGNet
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- MLP
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- ModifiedMLP
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- PirateNet
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- DeepONet
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- DeepPhyLSTM
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- LorenzEmbedding
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- ChipDeepONets
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- AutoEncoder
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- CuboidTransformer
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- SFNONet
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- UNONet
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- TFNO1dNet
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- TFNO2dNet
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- TFNO3dNet
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show_root_heading: true
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heading_level: 3

‎docs/zh/api/data/dataset.md

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- RadarDataset
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- build_dataset
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- DGMRDataset
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- DarcyFlowDataset
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- SphericalSWEDataset
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show_root_heading: true

‎docs/zh/api/equation.md

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options:
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members:
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- PDE
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- FractionalPoisson
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- AllenCahn
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- Biharmonic
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- FractionalPoisson
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- HeatExchanger
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- Laplace
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- LinearElasticity

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