forked from mindspore-Ecosystem/mindspore
PINNs(Navier-Stokes) fix error of lambda
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yuyiyang_3418
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@ -302,13 +302,13 @@ Navier-Stokes equation scenario
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| uploaded Date | 6/7/2021 (month/day/year) |
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| MindSpore Version | 1.2.0 |
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| Dataset | cylinder nektar wake |
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| Training Parameters | epoch=18000, lr=0.01, batch size=500. See src/config.py for details |
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| Training Parameters | epoch=19500, lr=0.01, batch size=500. See src/config.py for details |
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| Optimizer | Adam |
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| Loss Function | src/NavierStokes/loss.py |
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| outputs | the velocity field (x and y component), presure, and the fitting of the Navier-Stokes equation (x and y component) |
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| Loss | 0.0007302 |
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| Loss | 0.00042734024 |
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| Speed | 99ms/step |
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| Total time | 4.9431 hours |
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| Total time | 5.355 hours |
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| Parameters | 3.1K |
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| Checkpoint for Fine tuning | 39K (.ckpt file) |
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@ -320,13 +320,13 @@ Navier-Stokes equation scenario
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| MindSpore Version | 1.2.0 |
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| Dataset | cylinder nektar wake |
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| Noise intensity of the training data | 0.01 |
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| Training Parameters | epoch=18000, lr=0.01, batch size=500. See src/config.py for details |
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| Training Parameters | epoch=19400, lr=0.01, batch size=500. See src/config.py for details |
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| Optimizer | Adam |
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| Loss Function | src/NavierStokes/loss.py |
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| outputs | the velocity field (x and y component), presure, and the fitting of the Navier-Stokes equation (x and y component) |
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| Loss | 0.001309 |
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| Loss | 0.00045599302 |
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| Speed | 100ms/step |
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| Total time | 5.0084 hours |
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| Total time | 5.3979 hours |
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| Parameters | 3.1K |
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| Checkpoint for Fine tuning | 39K (.ckpt file) |
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@ -354,8 +354,8 @@ Navier-Stokes equation scenario
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| MindSpore Version | 1.2.0 |
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| Dataset | cylinder nektar wake |
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| outputs | undermined coefficient $\lambda_1$ and $\lambda_2$ of the Naiver-Stokes equation |
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| error percentage of $\lambda_1$ | 0.2698% |
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| error percentage of $\lambda_2$ | 0.8558% |
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| error percentage of $\lambda_1$ | 0.2545% |
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| error percentage of $\lambda_2$ | 0.9312% |
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| Parameters | GPU |
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| ------------------------------------ | ------------------------------------------------------------ |
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@ -366,8 +366,8 @@ Navier-Stokes equation scenario
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| Dataset | cylinder nektar wake |
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| Noise intensity of the training data | 0.01 |
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| outputs | undermined coefficient $\lambda_1$ and $\lambda_2$ of the Naiver-Stokes equation |
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| error percentage of $\lambda_1$ | 0.3655% |
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| error percentage of $\lambda_2$ | 2.3851% |
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| error percentage of $\lambda_1$ | 0.2497% |
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| error percentage of $\lambda_2$ | 1.8279% |
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# [Description of Random Situation](#contents)
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@ -160,29 +160,29 @@ Navier-Stokes方程是流体力学中描述粘性牛顿流体的方程。针对N
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- 配置Schrodinger方程场景。
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```python
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'epoch':50000 #训练轮次
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'lr':0.0001 #学习率
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'N0':50 #训练集在初始条件处的采样点数量,对于NLS数据集，0<N0<=256
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'Nb':50 #训练集在边界条件处的采样点数量,对于NLS数据集，0<Nb<=201
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'Nf':20000 #训练时用于计算Schrodinger方程约束的配点数
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'num_neuron':100 #PINNs网络全连接隐藏层的神经元数量
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'seed':2 #随机种子
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'path':'./Data/NLS.mat' #数据集存储路径
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'ck_path':'./ckpoints/' #保存checkpoint文件(.ckpt)的路径
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'epoch':50000 # 训练轮次
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'lr':0.0001 # 学习率
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'N0':50 # 训练集在初始条件处的采样点数量,对于NLS数据集，0<N0<=256
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'Nb':50 # 训练集在边界条件处的采样点数量,对于NLS数据集，0<Nb<=201
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'Nf':20000 # 训练时用于计算Schrodinger方程约束的配点数
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'num_neuron':100 # PINNs网络全连接隐藏层的神经元数量
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'seed':2 # 随机种子
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'path':'./Data/NLS.mat' # 数据集存储路径
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'ck_path':'./ckpoints/' # 保存checkpoint文件(.ckpt)的路径
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```
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- 配置Navier-Stokes方程场景。
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```python
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'epoch':18000 # number of epochs in training
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'lr': 0.01 # learning rate
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'n_train':5000 # amount of training data
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'path':'./Data/cylinder_nektar_wake.mat' # data set path
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'noise':0.0 # noise intensity
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'num_neuron':20 # number of neurons in fully connected hidden layer
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'ck_path':'./navier_ckpoints/' # path to save checkpoint files (.ckpt)
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'seed':0 # random seed
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'batch_size':500 # batch size
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'epoch':18000 # 默认训练论次
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'lr': 0.01 # 学习率
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'n_train':5000 # 训练集数据量
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'path':'./Data/cylinder_nektar_wake.mat' # 数据集路径
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'noise':0.0 # 噪声强度
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'num_neuron':20 # 全连接隐藏层的神经元数量
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'ck_path':'./navier_ckpoints/' # 保存checkpoint文件(.ckpt)的路径
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'seed':1 # 随机种子
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'batch_size':500 # 训练批次大小
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```
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更多配置细节请参考脚本`config.py`。
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@ -202,7 +202,7 @@ Navier-Stokes方程场景
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- GPU处理器环境运行Navier-Stokes方程场景
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```bash
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python train.py --scenario='NavierStokes' --datapath=[DATAPATH] --noise=[NOISE] > train.log 2>&1 &
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python train.py --scenario='NavierStokes' --datapath=[DATAPATH] --noise=[NOISE] --epoch=[EPOCH] > train.log 2>&1 &
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```
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- 以上python命令将在后台运行。您可以通过train.log文件查看结果。
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@ -299,13 +299,13 @@ Navier-Stokes方程场景
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| 上传日期 | 2021-6-7 |
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| MindSpore版本 | 1.2.0 |
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| 数据集 | cylinder nektar wake |
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| 训练参数 | epoch=18000, lr=0.01, batch size=500. 详见src/config.py |
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| 训练参数 | epoch=19500, lr=0.01, batch size=500. 详见src/config.py |
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| 优化器 | Adam |
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| 损失函数 | src/NavierStokes/loss.py |
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| 输出 | 速度场(x分量、y分量)，压强，对Navier-Stokes方程的拟合(x分量、y分量) |
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| 损失 | 0.0007302 |
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| 损失 | 0.00042734024 |
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| 速度 | 99毫秒/步 |
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| 总时长 | 4.9431 小时 |
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| 总时长 | 5.355 小时 |
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| 参数 | 3.1K |
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| 微调检查点 | 39K (.ckpt文件) |
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@ -317,13 +317,13 @@ Navier-Stokes方程场景
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| MindSpore版本 | 1.2.0 |
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| 数据集 | cylinder nektar wake |
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| 训练集噪声强度 | 0.01 |
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| 训练参数 | epoch=18000, lr=0.01, batch size=500. 详见src/config.py |
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| 训练参数 | epoch=19400, lr=0.01, batch size=500. 详见src/config.py |
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| 优化器 | Adam |
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| 损失函数 | src/NavierStokes/loss.py |
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| 输出 | 速度场(x分量、y分量)，压强，对Navier-Stokes方程的拟合(x分量、y分量) |
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| 损失 | 0.001309 |
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| 损失 | 0.00045599302 |
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| 速度 | 100毫秒/步 |
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| 总时长 | 5.0084 小时 |
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| 总时长 | 5.3979 小时 |
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| 参数 | 3.1K |
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| 微调检查点 | 39K (.ckpt文件) |
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@ -351,8 +351,8 @@ Navier-Stokes方程场景
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| MindSpore 版本 | 1.2.0 |
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| 数据集 | cylinder nektar wake |
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| 输出 | Navier-Stokes方程的待定系数$\lambda_1$和$\lambda_2$ |
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| $\lambda_1$误差百分比 | 0.2698% |
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| $\lambda_2$误差百分比 | 0.8558% |
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| $\lambda_1$误差百分比 | 0.2545% |
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| $\lambda_2$误差百分比 | 0.9312% |
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| 参数 | GPU |
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| --------------------- | --------------------------------------------------- |
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@ -363,8 +363,8 @@ Navier-Stokes方程场景
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| 数据集 | cylinder nektar wake |
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| 训练集噪声强度 | 0.01 |
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| 输出 | Navier-Stokes方程的待定系数$\lambda_1$和$\lambda_2$ |
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| $\lambda_1$误差百分比 | 0.3655% |
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| $\lambda_2$误差百分比 | 2.3851% |
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| $\lambda_1$误差百分比 | 0.2497% |
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| $\lambda_2$误差百分比 | 1.8279% |
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# [随机情况说明](#目录)
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@ -22,5 +22,5 @@ config_Sch = {'epoch': 50000, 'lr': 0.0001, 'N0': 50, 'Nb': 50, 'Nf': 20000, 'nu
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'seed': 2, 'path': './Data/NLS.mat', 'ck_path': './ckpoints/'}
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# config for Navier-Stokes equation scenario
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config_navier = {'epoch': 18000, 'lr': 0.01, 'n_train': 5000, 'path': './Data/cylinder_nektar_wake.mat',
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'noise': 0.0, 'num_neuron': 20, 'ck_path': './navier_ckpoints/', 'seed': 0, 'batch_size': 500}
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config_navier = {'epoch': 19000, 'lr': 0.01, 'n_train': 5000, 'path': './Data/cylinder_nektar_wake.mat',
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'noise': 0.0, 'num_neuron': 20, 'ck_path': './navier_ckpoints/', 'seed': 1, 'batch_size': 500}
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