mindflow.pde.NavierStokes

class mindflow.pde.NavierStokes(model, re=100.0, loss_fn='mse')

基于PDEWithLoss定义的二维NavierStokes方程求解问题。

参数：

• model (mindspore.nn.Cell) - 用于训练的网络模型。

• re (float) - 雷诺数（流体惯性力与粘滞力的比值）。默认值： 100.0。

• loss_fn (Union[str, Cell]) - 损失函数。默认值： "mse"。

支持平台：

Ascend GPU

样例：

>>> from mindflow.pde import NavierStokes
>>> from mindspore import nn, ops
>>> class Net(nn.Cell):
...     def __init__(self, cin=3, cout=3, hidden=10):
...         super().__init__()
...         self.fc1 = nn.Dense(cin, hidden)
...         self.fc2 = nn.Dense(hidden, hidden)
...         self.fcout = nn.Dense(hidden, cout)
...         self.act = ops.Tanh()
...
...     def construct(self, x):
...         x = self.act(self.fc1(x))
...         x = self.act(self.fc2(x))
...         x = self.fcout(x)
...         return x
>>> model = Net()
>>> problem = NavierStokes(model)
>>> print(problem.pde())
momentum_x: u(x, y, t)Derivative(u(x, y, t), x) + v(x, y, t)Derivative(u(x, y, t), y) +
Derivative(p(x, y, t), x) + Derivative(u(x, y, t), t) - 0.00999999977648258Derivative(u(x, y, t), (x, 2)) -
0.00999999977648258Derivative(u(x, y, t), (y, 2))
Item numbers of current derivative formula nodes: 6
momentum_y: u(x, y, t)Derivative(v(x, y, t), x) + v(x, y, t)Derivative(v(x, y, t), y) +
Derivative(p(x, y, t), y) + Derivative(v(x, y, t), t) - 0.00999999977648258Derivative(v(x, y, t), (x, 2)) -
0.00999999977648258Derivative(v(x, y, t), (y, 2))
Item numbers of current derivative formula nodes: 6
continuty: Derivative(u(x, y, t), x) + Derivative(v(x, y, t), y)
Item numbers of current derivative formula nodes: 2
{'momentum_x': u(x, y, t)Derivative(u(x, y, t), x) + v(x, y, t)Derivative(u(x, y, t), y) +
Derivative(p(x, y, t), x) + Derivative(u(x, y, t), t) - 0.00999999977648258Derivative(u(x, y, t), (x, 2)) -
0.00999999977648258Derivative(u(x, y, t), (y, 2)),
'momentum_y': u(x, y, t)Derivative(v(x, y, t), x) + v(x, y, t)Derivative(v(x, y, t), y) +
Derivative(p(x, y, t), y) + Derivative(v(x, y, t), t) - 0.00999999977648258Derivative(v(x, y, t), (x, 2)) -
0.00999999977648258Derivative(v(x, y, t), (y, 2)),
'continuty': Derivative(u(x, y, t), x) + Derivative(v(x, y, t), y)}

pde()

抽象方法，基于sympy定义的二维NavierStokes控制方程。

返回：

dict，自定义sympy符号方程。