def test_scalar_p1_scaled_mesh():
# Make coarse mesh smaller than fine mesh
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshc.geometry.points *= 0.9
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, "CG", 1)
Vf = FunctionSpace(meshf, "CG", 1)
u = Expression("x[0] + 2*x[1] + 3*x[2]", degree=1)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc, Vf).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
# Now make coarse mesh larger than fine mesh
meshc.geometry.points *= 1.5
uc = interpolate(u, Vc)
mat = PETScDMCollection.create_transfer_matrix(Vc, Vf).mat()
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def test_mg_solver_laplace():
# Create meshes and function spaces
meshes = [UnitSquareMesh(N, N) for N in [16, 32, 64]]
V = [FunctionSpace(mesh, "Lagrange", 1) for mesh in meshes]
# Create variational problem on fine grid
u, v = TrialFunction(V[-1]), TestFunction(V[-1])
a = dot(grad(u), grad(v)) * dx
L = v * dx
bc0 = Function(V[-1])
bc = DirichletBC(V[-1], bc0, "on_boundary")
A, b = assemble_system(a, L, bc)
# Create collection of PETSc DM objects
dm_collection = PETScDMCollection(V)
# Create PETSc Krylov solver and set operator
solver = PETScKrylovSolver()
solver.set_operator(A)
# Set PETSc solver type
PETScOptions.set("ksp_type", "richardson")
PETScOptions.set("pc_type", "mg")
# Set PETSc MG type and levels
PETScOptions.set("pc_mg_levels", len(V))
PETScOptions.set("pc_mg_galerkin")
# Set smoother
PETScOptions.set("mg_levels_ksp_type", "chebyshev")
PETScOptions.set("mg_levels_pc_type", "jacobi")
# Set tolerance and monitor residual
PETScOptions.set("ksp_monitor_true_residual")
PETScOptions.set("ksp_atol", 1.0e-12)
PETScOptions.set("ksp_rtol", 1.0e-12)
solver.set_from_options()
# Get fine grid DM and attach fine grid DM to solver
solver.set_dm(dm_collection.get_dm(-1))
solver.set_dm_active(False)
# Solve
x = PETScVector()
solver.solve(x, b)
# Check multigrid solution against LU solver solution
solver = LUSolver(A) # noqa
x_lu = PETScVector()
solver.solve(x_lu, b)
assert round((x - x_lu).norm("l2"), 10) == 0
# Clear all PETSc options
from petsc4py import PETSc
opts = PETSc.Options()
for key in opts.getAll():
opts.delValue(key)
def test_vector_p1_3d():
meshc = UnitCubeMesh(MPI.comm_world, 2, 3, 4)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = VectorFunctionSpace(meshc, ("CG", 1))
Vf = VectorFunctionSpace(meshf, ("CG", 1))
def u(x):
values0 = x[:, 0] + 2.0 * x[:, 1]
values1 = 4.0 * x[:, 0]
values2 = 3.0 * x[:, 2] + x[:, 0]
return np.stack([values0, values1, values2], axis=1)
uc, uf = Function(Vc), Function(Vf)
uc.interpolate(u)
uf.interpolate(u)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
def test_vector_p1_3d():
meshc = UnitCubeMesh(MPI.comm_world, 2, 3, 4)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = VectorFunctionSpace(meshc, ("CG", 1))
Vf = VectorFunctionSpace(meshf, ("CG", 1))
@function.expression.numba_eval
def expr_eval(values, x, cell_idx):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1]
values[:, 1] = 4.0 * x[:, 0]
values[:, 2] = 3.0 * x[:, 2] + x[:, 0]
u = Expression(expr_eval, shape=(3, ))
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector(), Vuc.vector())
diff = Vuc.vector()
diff.axpy(-1, uf.vector())
assert diff.norm() < 1.0e-12
def test_taylor_hood_cube():
pytest.xfail("Problem with Mixed Function Spaces")
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Ve = VectorElement("CG", meshc.ufl_cell(), 2)
Qe = FiniteElement("CG", meshc.ufl_cell(), 1)
Ze = MixedElement([Ve, Qe])
Zc = FunctionSpace(meshc, Ze)
Zf = FunctionSpace(meshf, Ze)
def z(values, x):
values[:, 0] = x[:, 0] * x[:, 1]
values[:, 1] = x[:, 1] * x[:, 2]
values[:, 2] = x[:, 2] * x[:, 0]
values[:, 3] = x[:, 0] + 3.0 * x[:, 1] + x[:, 2]
zc = interpolate(z, Zc)
zf = interpolate(z, Zf)
mat = PETScDMCollection.create_transfer_matrix(Zc, Zf)
Zuc = Function(Zf)
mat.mult(zc.vector, Zuc.vector)
Zuc.vector.update_ghost_values()
diff = Function(Zf)
diff.assign(Zuc - zf)
assert diff.vector.norm("l2") < 1.0e-12
def test_taylor_hood_cube():
pytest.xfail("Problem with Mixed Function Spaces")
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Ve = VectorElement("CG", meshc.ufl_cell(), 2)
Qe = FiniteElement("CG", meshc.ufl_cell(), 1)
Ze = MixedElement([Ve, Qe])
Zc = FunctionSpace(meshc, Ze)
Zf = FunctionSpace(meshf, Ze)
z = Expression(
("x[0]*x[1]", "x[1]*x[2]", "x[2]*x[0]", "x[0] + 3*x[1] + x[2]"),
degree=2)
zc = interpolate(z, Zc)
zf = interpolate(z, Zf)
mat = PETScDMCollection.create_transfer_matrix(Zc, Zf)
Zuc = Function(Zf)
mat.mult(zc.vector(), Zuc.vector())
Zuc.vector().update_ghost_values()
diff = Function(Zf)
diff.assign(Zuc - zf)
assert diff.vector().norm("l2") < 1.0e-12
def test_scalar_p1_scaled_mesh():
# Make coarse mesh smaller than fine mesh
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshc.geometry.points *= 0.9
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, ("CG", 1))
Vf = FunctionSpace(meshf, ("CG", 1))
@function.expression.numba_eval
def expr_eval(values, x, cell_idx):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1] + 3.0 * x[:, 2]
u = Expression(expr_eval)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector(), Vuc.vector())
diff = Vuc.vector()
diff.axpy(-1, uf.vector())
assert diff.norm() < 1.0e-12
# Now make coarse mesh larger than fine mesh
meshc.geometry.points *= 1.5
uc = interpolate(u, Vc)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
mat.mult(uc.vector(), Vuc.vector())
diff = Vuc.vector()
diff.axpy(-1, uf.vector())
assert diff.norm() < 1.0e-12
def test_scalar_p1_scaled_mesh():
# Make coarse mesh smaller than fine mesh
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshc.geometry.points *= 0.9
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, ("CG", 1))
Vf = FunctionSpace(meshf, ("CG", 1))
def u(x):
return x[:, 0] + 2.0 * x[:, 1] + 3.0 * x[:, 2]
uc, uf = Function(Vc), Function(Vf)
uc.interpolate(u)
uf.interpolate(u)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
# Now make coarse mesh larger than fine mesh
meshc.geometry.points *= 1.5
uc.interpolate(u)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
def test_vector_p1_3d():
meshc = UnitCubeMesh(MPI.comm_world, 2, 3, 4)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = VectorFunctionSpace(meshc, "CG", 1)
Vf = VectorFunctionSpace(meshf, "CG", 1)
u = Expression(("x[0] + 2*x[1]", "4*x[0]", "3*x[2] + x[0]"), degree=1)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc, Vf).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def test_vector_p2_2d():
meshc = UnitSquareMesh(MPI.comm_world, 5, 4)
meshf = UnitSquareMesh(MPI.comm_world, 5, 8)
Vc = VectorFunctionSpace(meshc, ("CG", 2))
Vf = VectorFunctionSpace(meshf, ("CG", 2))
u = Expression(("x[0] + 2*x[1]*x[0]", "4*x[0]*x[1]"), degree=2)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def test_scalar_p2():
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, ("CG", 2))
Vf = FunctionSpace(meshf, ("CG", 2))
u = Expression("x[0]*x[2] + 2*x[1]*x[0] + 3*x[2]", degree=2)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def test_scalar_p2():
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, ("CG", 2))
Vf = FunctionSpace(meshf, ("CG", 2))
def u(values, x):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1] + 3.0 * x[:, 2]
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
def test_vector_p2_2d():
meshc = UnitSquareMesh(MPI.comm_world, 5, 4)
meshf = UnitSquareMesh(MPI.comm_world, 5, 8)
Vc = VectorFunctionSpace(meshc, ("CG", 2))
Vf = VectorFunctionSpace(meshf, ("CG", 2))
def u(values, x):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1]
values[:, 1] = 4.0 * x[:, 0] * x[:, 1]
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
def test_vector_p2_2d():
meshc = UnitSquareMesh(MPI.comm_world, 5, 4)
meshf = UnitSquareMesh(MPI.comm_world, 5, 8)
Vc = VectorFunctionSpace(meshc, ("CG", 2))
Vf = VectorFunctionSpace(meshf, ("CG", 2))
def u(x):
return np.stack([x[:, 0] + 2.0 * x[:, 1], 4.0 * x[:, 0] * x[:, 1]],
axis=1)
uc, uf = Function(Vc), Function(Vf)
uc.interpolate(u)
uf.interpolate(u)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object)
Vuc = Function(Vf)
mat.mult(uc.vector, Vuc.vector)
diff = Vuc.vector
diff.axpy(-1, uf.vector)
assert diff.norm() < 1.0e-12
def test_vector_p2_2d():
meshc = UnitSquareMesh(MPI.comm_world, 5, 4)
meshf = UnitSquareMesh(MPI.comm_world, 5, 8)
Vc = VectorFunctionSpace(meshc, ("CG", 2))
Vf = VectorFunctionSpace(meshf, ("CG", 2))
@function.expression.numba_eval
def expr_eval(values, x, cell_idx):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1]
values[:, 1] = 4.0 * x[:, 0] * x[:, 1]
u = Expression(expr_eval, shape=(2, ))
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def test_scalar_p2():
meshc = UnitCubeMesh(MPI.comm_world, 2, 2, 2)
meshf = UnitCubeMesh(MPI.comm_world, 3, 4, 5)
Vc = FunctionSpace(meshc, ("CG", 2))
Vf = FunctionSpace(meshf, ("CG", 2))
@function.expression.numba_eval
def expr_eval(values, x, cell_idx):
values[:, 0] = x[:, 0] + 2.0 * x[:, 1] + 3.0 * x[:, 2]
u = Expression(expr_eval)
uc = interpolate(u, Vc)
uf = interpolate(u, Vf)
mat = PETScDMCollection.create_transfer_matrix(Vc._cpp_object,
Vf._cpp_object).mat()
Vuc = Function(Vf)
mat.mult(uc.vector().vec(), Vuc.vector().vec())
diff = Vuc.vector()
diff.vec().axpy(-1, uf.vector().vec())
assert diff.norm(Norm.l2) < 1.0e-12
def xtest_mg_solver_stokes():
mesh0 = UnitCubeMesh(2, 2, 2)
mesh1 = UnitCubeMesh(4, 4, 4)
mesh2 = UnitCubeMesh(8, 8, 8)
Ve = VectorElement("CG", mesh0.ufl_cell(), 2)
Qe = FiniteElement("CG", mesh0.ufl_cell(), 1)
Ze = MixedElement([Ve, Qe])
Z0 = FunctionSpace(mesh0, Ze)
Z1 = FunctionSpace(mesh1, Ze)
Z2 = FunctionSpace(mesh2, Ze)
W = Z2
# Boundaries
def right(x, on_boundary):
return x[0] > (1.0 - DOLFIN_EPS)
def left(x, on_boundary):
return x[0] < DOLFIN_EPS
def top_bottom(x, on_boundary):
return x[1] > 1.0 - DOLFIN_EPS or x[1] < DOLFIN_EPS
# No-slip boundary condition for velocity
noslip = Constant((0.0, 0.0, 0.0))
bc0 = DirichletBC(W.sub(0), noslip, top_bottom)
# Inflow boundary condition for velocity
inflow = Expression(("-sin(x[1]*pi)", "0.0", "0.0"), degree=2)
bc1 = DirichletBC(W.sub(0), inflow, right)
# Collect boundary conditions
bcs = [bc0, bc1]
# Define variational problem
(u, p) = TrialFunctions(W)
(v, q) = TestFunctions(W)
f = Constant((0.0, 0.0, 0.0))
a = inner(grad(u), grad(v)) * dx + div(v) * p * dx + q * div(u) * dx
L = inner(f, v) * dx
# Form for use in constructing preconditioner matrix
b = inner(grad(u), grad(v)) * dx + p * q * dx
# Assemble system
A, bb = assemble_system(a, L, bcs)
# Assemble preconditioner system
P, btmp = assemble_system(b, L, bcs)
spaces = [Z0, Z1, Z2]
dm_collection = PETScDMCollection(spaces)
solver = PETScKrylovSolver()
solver.set_operators(A, P)
PETScOptions.set("ksp_type", "gcr")
PETScOptions.set("pc_type", "mg")
PETScOptions.set("pc_mg_levels", 3)
PETScOptions.set("pc_mg_galerkin")
PETScOptions.set("ksp_monitor_true_residual")
PETScOptions.set("ksp_atol", 1.0e-10)
PETScOptions.set("ksp_rtol", 1.0e-10)
solver.set_from_options()
from petsc4py import PETSc
ksp = solver.ksp()
ksp.setDM(dm_collection.dm())
ksp.setDMActive(False)
x = PETScVector()
solver.solve(x, bb)
# Check multigrid solution against LU solver
solver = LUSolver(A) # noqa
x_lu = PETScVector()
solver.solve(x_lu, bb)
assert round((x - x_lu).norm("l2"), 10) == 0
# Clear all PETSc options
opts = PETSc.Options()
for key in opts.getAll():
opts.delValue(key)
