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[Doc] Update document #782

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21 changes: 11 additions & 10 deletions README.md
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Expand Up @@ -52,7 +52,7 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
| 飞行器设计 | [MeshGraphNets](https://aistudio.baidu.com/projectdetail/5322713) | 数据驱动 | GNN | 监督学习 | [Data](https://aistudio.baidu.com/datasetdetail/184320) | [Paper](https://arxiv.org/abs/2010.03409)|
| 飞行器设计 | [火箭发动机真空羽流](https://aistudio.baidu.com/projectdetail/4486133) | 数据驱动 | CNN | 监督学习 | [Data](https://aistudio.baidu.com/datasetdetail/167250) | - |
| 飞行器设计 | [Deep-Flow-Prediction](https://aistudio.baidu.com/projectdetail/5671596) | 数据驱动 | TurbNetG | 监督学习 | [Data](https://aistudio.baidu.com/datasetdetail/197778) | [Paper](https://arxiv.org/abs/1810.08217) |
| 通用流场模拟 | [气动外形设计](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/amgnet/) | 数据驱动 | AMGNet | 监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/AMGNet/data.zip) | [Paper](https://arxiv.org/abs/1810.08217) |
| 通用流场模拟 | [气动外形设计](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/amgnet) | 数据驱动 | AMGNet | 监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/AMGNet/data.zip) | [Paper](https://arxiv.org/abs/1810.08217) |
| 流固耦合 | [涡激振动](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/viv) | 机理驱动 | MLP | 半监督学习 | [Data](https://github.com/PaddlePaddle/PaddleScience/blob/develop/examples/fsi/VIV_Training_Neta100.mat) | [Paper](https://arxiv.org/abs/2206.03864)|
| 多相流 | [气液两相流](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/bubble) | 机理驱动 | BubbleNet | 半监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/BubbleNet/bubble.mat) | [Paper](https://pubs.aip.org/aip/adv/article/12/3/035153/2819394/Predicting-micro-bubble-dynamics-with-semi-physics)|
| 多相流 | [twophasePINN](https://aistudio.baidu.com/projectdetail/5379212) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://doi.org/10.1016/j.mlwa.2021.100029)|
Expand All @@ -62,22 +62,22 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
| 流场高分辨率重构 | [基于Voronoi嵌入辅助深度学习的稀疏传感器全局场重建](https://aistudio.baidu.com/projectdetail/5807904) | 数据驱动 | CNN | 监督学习 | [Data1](https://drive.google.com/drive/folders/1K7upSyHAIVtsyNAqe6P8TY1nS5WpxJ2c)<br>[Data2](https://drive.google.com/drive/folders/1pVW4epkeHkT2WHZB7Dym5IURcfOP4cXu)<br>[Data3](https://drive.google.com/drive/folders/1xIY_jIu-hNcRY-TTf4oYX1Xg4_fx8ZvD) | [Paper](https://arxiv.org/pdf/2202.11214.pdf) |
| 流场高分辨率重构 | 基于扩散的流体超分重构<sup>coming soon</sup> | 数理融合 | DDPM | 监督学习 | - | [Paper](https://www.sciencedirect.com/science/article/pii/S0021999123000670)|
| 求解器耦合 | [CFD-GCN](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/cfdgcn) | 数据驱动 | GCN | 监督学习 | [Data](https://aistudio.baidu.com/aistudio/datasetdetail/184778)<br>[Mesh](https://paddle-org.bj.bcebos.com/paddlescience/datasets/CFDGCN/meshes.tar) | [Paper](https://arxiv.org/abs/2007.04439)|
| 受力分析 | [1D 欧拉梁变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/euler_beam/) | 机理驱动 | MLP | 无监督学习 | - | - |
| 受力分析 | [1D 欧拉梁变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/euler_beam) | 机理驱动 | MLP | 无监督学习 | - | - |
| 受力分析 | [2D 平板变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/biharmonic2d) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://arxiv.org/abs/2108.07243) |
| 受力分析 | [3D 连接件变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/bracket) | 机理驱动 | MLP | 无监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/bracket/bracket_dataset.tar) | [Tutorial](https://docs.nvidia.com/deeplearning/modulus/modulus-v2209/user_guide/foundational/linear_elasticity.html) |
| 受力分析 | [结构震动模拟](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/phylstm) | 机理驱动 | PhyLSTM | 监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyLSTM/data_boucwen.mat) | [Paper](https://arxiv.org/abs/2002.10253) |
| 受力分析 | [2D 弹塑性结构](https://paddlescience-docs.readthedocs.io/zh/examples/epnn.md) | 机理驱动 | EPNN | 无监督学习 | [Train Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/epnn/dstate-16-plas.dat)<br>[Eval Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/epnn/dstress-16-plas.dat) | [Paper](https://arxiv.org/abs/2204.12088) |
| 受力分析和逆问题 | [3D 汽车控制臂变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/control_arm.md) | 机理驱动 | MLP | 无监督学习 | - | - |
| 拓扑优化 | [2D 拓扑优化](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/topopt.md) | 数据驱动 | TopOptNN | 监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/topopt/top_dataset.h5) | [Paper](https://arxiv.org/pdf/1709.09578) |
| 热仿真 | [1D 换热器热仿真](https://paddlescience-docs.readthedocs.io/zh/examples/heat_exchanger.md) | 机理驱动 | PI-DeepONet | 无监督学习 | - | - |
| 热仿真 | [2D 热仿真](https://paddlescience-docs.readthedocs.io/zh/examples/heat_pinn.md) | 机理驱动 | PINN | 无监督学习 | - | [Paper](https://arxiv.org/abs/1711.10561)|
| 受力分析 | [2D 弹塑性结构](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/epnn) | 机理驱动 | EPNN | 无监督学习 | [Train Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/epnn/dstate-16-plas.dat)<br>[Eval Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/epnn/dstress-16-plas.dat) | [Paper](https://arxiv.org/abs/2204.12088) |
| 受力分析和逆问题 | [3D 汽车控制臂变形](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/control_arm) | 机理驱动 | MLP | 无监督学习 | - | - |
| 拓扑优化 | [2D 拓扑优化](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/topopt) | 数据驱动 | TopOptNN | 监督学习 | [Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/topopt/top_dataset.h5) | [Paper](https://arxiv.org/pdf/1709.09578) |
| 热仿真 | [1D 换热器热仿真](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/heat_exchanger) | 机理驱动 | PI-DeepONet | 无监督学习 | - | - |
| 热仿真 | [2D 热仿真](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/heat_pinn) | 机理驱动 | PINN | 无监督学习 | - | [Paper](https://arxiv.org/abs/1711.10561)|

<br>
<p align="center"><b>材料科学(AI for Material)</b></p>

| 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 |
|-----|---------|-----|---------|----|---------|---------|
| 材料设计 | [散射板设计(反问题)](./zh/examples/hpinns.md) | 数理融合 | 数据驱动 | 监督学习 | [Train Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/hPINNs/hpinns_holo_train.mat)<br>[Eval Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/hPINNs/hpinns_holo_valid.mat) | [Paper](https://arxiv.org/pdf/2102.04626.pdf) |
| 材料设计 | [散射板设计(反问题)](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/hpinns) | 数理融合 | 数据驱动 | 监督学习 | [Train Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/hPINNs/hpinns_holo_train.mat)<br>[Eval Data](https://paddle-org.bj.bcebos.com/paddlescience/datasets/hPINNs/hpinns_holo_valid.mat) | [Paper](https://arxiv.org/pdf/2102.04626.pdf) |
| 材料生成 | 面向对称感知的周期性材料生成<sup>coming soon</sup> | 数据驱动 | SyMat | 监督学习 | - | - |

<br>
Expand All @@ -86,15 +86,16 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
| 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 |
|-----|---------|-----|---------|----|---------|---------|
| 天气预报 | [FourCastNet 气象预报](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/fourcastnet) | 数据驱动 | FourCastNet | 监督学习 | [ERA5](https://app.globus.org/file-manager?origin_id=945b3c9e-0f8c-11ed-8daf-9f359c660fbd&origin_path=%2F~%2Fdata%2F) | [Paper](https://arxiv.org/pdf/2202.11214.pdf) |
| 天气预报 | [NowCastNet 气象预报](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/nowcastnet) | 数据驱动 | NowCastNet | 监督学习 | [MRMS](https://app.globus.org/file-manager?origin_id=945b3c9e-0f8c-11ed-8daf-9f359c660fbd&origin_path=%2F~%2Fdata%2F) | [Paper](https://www.nature.com/articles/s41586-023-06184-4) |
| 天气预报 | GraphCast 气象预报<sup>coming soon</sup> | 数据驱动 | GraphCastNet* | 监督学习 | - | [Paper](https://arxiv.org/pdf/2202.11214.pdf) |
| 大气污染物 | [UNet 污染物扩散](https://aistudio.baidu.com/projectdetail/5663515?channel=0&channelType=0&sUid=438690&shared=1&ts=1698221963752) | 数据驱动 | UNet | 监督学习 | [Data](https://aistudio.baidu.com/datasetdetail/198102) | - |

<!-- --8<-- [start:update] -->
## 🕘最近更新

- 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) 。
- 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))。
- 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))。
- 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))。
- 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))。
- 针对串联方程和复杂方程场景，`Equation` 模块支持基于 [sympy](https://docs.sympy.org/dev/tutorials/intro-tutorial/intro.html) 的符号计算，并支持和 python 函数混合使用([#507](https://github.com/PaddlePaddle/PaddleScience/pull/507)、[#505](https://github.com/PaddlePaddle/PaddleScience/pull/505))。
- `Geometry` 模块和 `InteriorConstraint`、`InitialConstraint` 支持计算 SDF 微分功能([#539](https://github.com/PaddlePaddle/PaddleScience/pull/539))。
- 添加 **M**ulti**T**ask**L**earning(`ppsci.loss.mtl`) 多任务学习模块，针对多任务优化(如 PINN 方法)进一步提升性能，使用方式：[多任务学习指南](https://paddlescience-docs.readthedocs.io/zh/latest/zh/user_guide/#24)([#493](https://github.com/PaddlePaddle/PaddleScience/pull/505)、[#492](https://github.com/PaddlePaddle/PaddleScience/pull/505))。
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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 |
|-----|---------|-----|---------|----|---------|---------|
| 天气预报 | [FourCastNet 气象预报](./zh/examples/fourcastnet.md) | 数据驱动 | FourCastNet | 监督学习 | [ERA5](https://app.globus.org/file-manager?origin_id=945b3c9e-0f8c-11ed-8daf-9f359c660fbd&origin_path=%2F~%2Fdata%2F) | [Paper](https://arxiv.org/pdf/2202.11214.pdf) |
| 天气预报 | [NowCastNet 气象预报](./zh/examples/nowcastnet.md) | 数据驱动 | NowCastNet | 监督学习 | [MRMS](https://app.globus.org/file-manager?origin_id=945b3c9e-0f8c-11ed-8daf-9f359c660fbd&origin_path=%2F~%2Fdata%2F) | [Paper](https://www.nature.com/articles/s41586-023-06184-4) |
| 天气预报 | GraphCast 气象预报<sup>coming soon</sup> | 数据驱动 | GraphCastNet* | 监督学习 | - | [Paper](https://arxiv.org/abs/2212.12794) |
| 大气污染物 | [UNet 污染物扩散](https://aistudio.baidu.com/projectdetail/5663515?channel=0&channelType=0&sUid=438690&shared=1&ts=1698221963752) | 数据驱动 | UNet | 监督学习 | [Data](https://aistudio.baidu.com/datasetdetail/198102) | - |

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Expand Up @@ -52,7 +52,7 @@ examples/nowcastnet/conf/nowcastnet.yaml:35:53

其中，`input_keys` 和 `output_keys` 分别代表网络模型输入、输出变量的名称。

## 4 模型评估可视化
## 4. 模型评估可视化

完成上述设置之后，将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`：

Expand Down Expand Up @@ -91,3 +91,7 @@ examples/nowcastnet/nowcastnet.py
![result](https://paddle-org.bj.bcebos.com/paddlescience/docs/nowcastnet/gt.gif){ loading=lazy style="margin:0 auto;"}
<figcaption>模型真值结果</figcaption>
</figure>

## 7. 参考资料

- [Skilful nowcasting of extreme precipitation with NowcastNet](https://www.nature.com/articles/s41586-023-06184-4)