@@ -79,11 +79,9 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
7979| 流固耦合 | [ 涡激振动] ( https://paddlescience-docs.readthedocs.io/zh-cn/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 ) |
8080| 多相流 | [ 气液两相流] ( https://paddlescience-docs.readthedocs.io/zh-cn/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 ) |
8181| 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 ) |
82- | 多相流 | 非高斯渗透率场估计<sup >coming soon</sup > | 机理驱动 | cINN | 监督学习 | - | [ Paper] ( https://pubs.aip.org/aip/adv/article/12/3/035153/2819394/Predicting-micro-bubble-dynamics-with-semi-physics ) |
8382| 流场高分辨率重构 | [ 2D 湍流流场重构] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/tempoGAN ) | 数据驱动 | tempoGAN | 监督学习 | [ Train Data] ( https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_train.mat ) <br >[ Eval Data] ( https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_valid.mat ) | [ Paper] ( https://dl.acm.org/doi/10.1145/3197517.3201304 ) |
8483| 流场高分辨率重构 | [ 2D 湍流流场重构] ( https://aistudio.baidu.com/projectdetail/4493261?contributionType=1 ) | 数据驱动 | cycleGAN | 监督学习 | [ Train Data] ( https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_train.mat ) <br >[ Eval Data] ( https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_valid.mat ) | [ Paper] ( https://arxiv.org/abs/2007.15324 ) |
8584| 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 ) |
86- | 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 ) |
8785| 求解器耦合 | [ CFD-GCN] ( https://paddlescience-docs.readthedocs.io/zh-cn/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 ) |
8886| 受力分析 | [ 1D 欧拉梁变形] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/euler_beam ) | 机理驱动 | MLP | 无监督学习 | - | - |
8987| 受力分析 | [ 2D 平板变形] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/biharmonic2d ) | 机理驱动 | MLP | 无监督学习 | - | [ Paper] ( https://arxiv.org/abs/2108.07243 ) |
@@ -102,7 +100,6 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
102100| 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 |
103101| -----| ---------| -----| ---------| ----| ---------| ---------|
104102| 材料设计 | [ 散射板设计(反问题)] ( https://paddlescience-docs.readthedocs.io/zh-cn/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 ) |
105- | 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 | - | - |
106103
107104<br >
108105<p align =" center " ><b >地球科学(AI for Earth Science)</b ></p >
@@ -115,15 +112,17 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
115112| 天气预报 | [ GraphCast 气象预报] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/graphcast ) | 数据驱动 | GraphCastNet | 监督学习 | - | [ Paper] ( https://arxiv.org/abs/2212.12794 ) |
116113| 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 ) | - |
117114| 天气预报 | [ DGMR 气象预报] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/dgmr.md ) | 数据驱动 | DGMR | 监督学习 | [ UK dataset] ( https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km ) | [ Paper] ( https://arxiv.org/pdf/2104.00954.pdf ) |
115+ | 地震波形反演 | [ VelocityGAN 地震波形反演] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/velocity_gan.md ) | 数据驱动 | VelocityGAN | 监督学习 | [ OpenFWI] ( https://openfwi-lanl.github.io/docs/data.html#vel ) | [ Paper] ( https://arxiv.org/abs/1809.10262v6 ) |
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120118## 🕘最近更新
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120+ - 基于 PaddleScience 的 ADR 方程求解方法 [ Physics-informed neural networks for advection–diffusion–Langmuir adsorption processes] ( https://doi.org/10.1063/5.0221924 ) 被 Physics of Fluids 2024 接受。
122121- 添加 [ IJCAI 2024: 任意三维几何外形车辆的风阻快速预测竞赛] ( https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5 ) ，track A, B, C 的 paddle/pytorch 代码链接。
123122- 添加 SPINN(基于 Helmholtz3D 方程求解) [ helmholtz3d] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/spinn/ ) 。
124123- 添加 CVit(基于 Advection 方程和 N-S 方程求解) [ CVit(Navier-Stokes)] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/ns_cvit/ ) 、[ CVit(Advection)] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/adv_cvit/ ) 。
125124- 添加 PirateNet(基于 Allen-cahn 方程和 N-S 方程求解) [ Allen-Cahn] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/allen_cahn/ ) 、[ LDC2D(Re3200)] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/ldc2d_steady/ ) 。
126- - 基于 PaddleScience 的快速热仿真方法 [ A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network] ( https://pubs.aip. org/aip/pof/article-abstract/36/3/037142/3277890/A-fast-general-thermal-simulation-model-based-on?redirectedFrom=fulltext ) 被 Physics of Fluids 2024 接受。
125+ - 基于 PaddleScience 的快速热仿真方法 [ A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network] ( https://doi. org/10.1063/5.0194245 ) 被 Physics of Fluids 2024 接受。
127126- 添加多目标优化算法 [ Relobralo] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/api/loss/mtl/#ppsci.loss.mtl.Relobralo ) 。
128127- 添加气泡流求解案例([ Bubble] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/bubble ) )、机翼优化案例([ DeepCFD] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/deepcfd/ ) )、热传导仿真案例([ HeatPINN] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/heat_pinn ) )、非线性短临预报模型([ Nowcasting(仅推理)] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/nowcastnet ) )、拓扑优化案例([ TopOpt] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/topopt ) )、矩形平板线弹性方程求解案例([ Biharmonic2D] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/biharmonic2d ) )。
129128- 添加二维血管案例([ LabelFree-DNN-Surrogate] ( https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/labelfree_DNN_surrogate/#4 ) )、空气激波案例([ ShockWave] ( https://paddlescience-docs.readthedocs.io/zh-cn/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-cn/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 ) )。
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