def forward(self):
"""
Forwarding discrete solver.
"""
# Setting potential rhs.
if self.config.rhs is None:
self.ustar.fill(0.)
else:
raise NotImplementedError()
# Appending previous steps contributions.
fe_op.mlt_add(self.operator2, self.u2, self.ustar)
fe_op.mlt_add(self.operator1, self.u1, self.ustar)
# Appending potential boundary condition contributions.
self.__add_boundary_contrib_prediction(
self.config.left_boundary_condition, self.fe_space.get_left_idx())
self.__add_boundary_contrib_prediction(
self.config.right_boundary_condition,
self.fe_space.get_right_idx())
# Applying invert operator.
fe_op.mlt(self.inv_operator, self.ustar, self.u0)
def test_mlt_add():
"""
Testing addition & multiplication operation on finite element operators.
"""
# Testing assembled case.
op = fe_op.make_from_data(np.diag(np.array([1.0, 2.0, 3.0])),
fe_op.AssemblyType.ASSEMBLED)
u = np.array([1.0, 1.0, 1.0])
v = np.array([2.0, 3.0, 4.0])
fe_op.mlt_add(op, u, v, coef=2.0)
np_test.assert_array_almost_equal(v, [4.0, 7.0, 10.0])
# Testing lumped case.
op = fe_op.make_from_data(np.array([1.0, 2.0, 3.0]),
fe_op.AssemblyType.LUMPED)
u = np.array([1.0, 1.0, 1.0])
v = np.array([2.0, 3.0, 4.0])
fe_op.mlt_add(op, u, v)
np_test.assert_array_almost_equal(v, [3.0, 5.0, 7.0])
def forward(self):
"""
Forwarding discrete solver.
"""
# Setting potential rhs.
self.ustar.fill(0.)
if self.config.rhs is not None:
fe_op.mlt_add(self.rhs_operator,
self.config.rhs(self.fe_space, self.time),
self.ustar, self.timestep**2)
# Appending previous steps contributions.
fe_op.mlt_add(self.operator2, self.u2, self.ustar)
fe_op.mlt_add(self.operator1, self.u1, self.ustar)
# Appending potential boundary condition contributions.
self.__add_boundary_contrib_prediction(
self.config.left_boundary_condition, self.fe_space.get_left_idx())
self.__add_boundary_contrib_prediction(
self.config.right_boundary_condition,
self.fe_space.get_right_idx())
# Applying invert operator.
fe_op.mlt(self.inv_operator, self.ustar, self.u0)
def forward(self):
"""
Forwarding discrete solver.
"""
# Initializing predictions.
self.ustar.fill(0.)
self.sstar.fill(0.)
# Appending previous steps contributions into displacement prediction.
fe_op.mlt_add(self.operator2_u, self.u2, self.ustar)
fe_op.mlt_add(self.operator1_u, self.u1, self.ustar)
fe_op.mlt_add(self.transposed_gradient,
self.s1,
self.ustar,
coef=-self.timestep**2)
# Appending potential boundary condition contributions.
self.__add_boundary_contrib_prediction(
self.config.left_boundary_condition, self.fe_space.get_left_idx())
self.__add_boundary_contrib_prediction(
self.config.right_boundary_condition,
self.fe_space.get_right_idx())
# Applying inverse operator on displacement prediction.
fe_op.mlt(self.inv_operator_u, self.ustar, self.u0)
# Appending previous step contribution into internal variable prediction.
fe_op.mlt_add(self.operator1_s, self.s1, self.sstar)
fe_op.mlt_add(self.gradient, self.u0, self.sstar, coef=1.0)
fe_op.mlt_add(self.gradient, self.u1, self.sstar, coef=-1.0)
# Applying inverse operator on internal variable predication.
fe_op.mlt(self.inv_operator_s, self.sstar, self.s0)
def rhs_d2theta(fe_space, time):
rhs = np.zeros(fe_space.get_ndof())
fe_op.mlt_add(minv_k, propag_dtheta.u1, rhs, -2.0)
return rhs
