mindflow.pde.Poisson

class mindflow.pde.Poisson(model, loss_fn='mse')

Base class for Poisson 2-D problem based on PDEWithLoss.

Parameters

• model (mindspore.nn.Cell) – network for training.

• loss_fn (Union[str, Cell]) – Define the loss function. Default: "mse".

Supported Platforms:

Ascend GPU

Examples

>>> from mindflow.pde import Poisson
>>> from mindspore import nn, ops
>>> class Net(nn.Cell):
...     def __init__(self, cin=2, cout=1, 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 = Poisson(model)
>>> print(problem.pde())
poisson: Derivative(u(x, y), (x, 2)) + Derivative(u(x, y), (y, 2)) + 1.0
    Item numbers of current derivative formula nodes: 3
{'poisson': Derivative(u(x, y), (x, 2)) + Derivative(u(x, y), (y, 2)) + 1.0}

pde()

Define Poisson 2-D governing equations based on sympy, abstract method.

Returns

dict, user defined sympy symbolic equations.