我使用FEniCS对扩散方程进行了如下离散:
@BindsOptionalOf
我正常运行GMRES解算器。回调参数是我定义的单独迭代计数器。
def DiscretiseEquation(h):
mesh = UnitSquareMesh(h, h)
V = FunctionSpace(mesh, 'Lagrange', 1)
def on_boundary(x, on_boundary):
return on_boundary
bc_value = Constant(0.0)
boundary_condition = DirichletBC(V, bc_value, on_boundary)
class RandomDiffusionField(Expression):
def __init__(self, m, n, element):
self._rand_field = np.exp(-np.random.randn(m, n))
self._m = m
self._n = n
self._ufl_element = element
def eval(self, value, x):
x_index = np.int(np.floor(self._m * x[0]))
y_index = np.int(np.floor(self._n * x[1]))
i = min(x_index, self._m - 1)
j = min(y_index, self._n - 1)
value[0] = self._rand_field[i, j]
def value_shape(self):
return(1, )
class RandomRhs(Expression):
def __init__(self, m, n, element):
self._rand_field = np.random.randn(m, n)
self._m = m
self._n = n
self._ufl_element = element
def eval(self, value, x):
x_index = np.int(np.floor(self._m * x[0]))
y_index = np.int(np.floor(self._n * x[1]))
i = min(x_index, self._m - 1)
j = min(y_index, self._n - 1)
value[0] = self._rand_field[i, j]
def value_shape(self):
return (1, )
u = TrialFunction(V)
v = TestFunction(V)
random_field = RandomDiffusionField(100, 100, element=V.ufl_element())
zero = Expression("0", element=V.ufl_element())
one = Expression("1", element=V.ufl_element())
diffusion = as_matrix(((random_field, zero), (zero, one)))
a = inner(diffusion * grad(u), grad(v)) * dx
L = RandomRhs(h, h, element=V.ufl_element()) * v * dx
A = assemble(a)
b = assemble(L)
boundary_condition.apply(A, b)
A = as_backend_type(A).mat()
(indptr, indices, data) = A.getValuesCSR()
mat = csr_matrix((data, indices, indptr), shape=A.size)
rhs = b.array()
#Solving
x = spsolve(mat, rhs)
#Conversion to a FEniCS function
u = Function(V)
u.vector()[:] = x
例程返回一个NameError,说明' mat'未定义:
DiscretiseEquation(100)
A = mat
b = rhs
x, info = gmres(A, b, callback = IterCount())
据我所知,应该在调用DiscretiseEquation函数时定义它?