def monolithic_solve():
"""Monolithic (interleaved) solver"""
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = dolfinx.FunctionSpace(mesh, TH)
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
a00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a = a00 + a01 + a10
p00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
p11 = ufl.inner(p, q) * dx
p_form = p00 + p11
f = dolfinx.Function(W.sub(0).collapse())
p_zero = dolfinx.Function(W.sub(1).collapse())
L0 = inner(f, v) * dx
L1 = inner(p_zero, q) * dx
L = L0 + L1
bdofsW0_P2_0 = dolfinx.fem.locate_dofs_topological(
(W.sub(0), P2), facetdim, bndry_facets0)
bdofsW0_P2_1 = dolfinx.fem.locate_dofs_topological(
(W.sub(0), P2), facetdim, bndry_facets1)
bc0 = dolfinx.DirichletBC(u0, bdofsW0_P2_0, W.sub(0))
bc1 = dolfinx.DirichletBC(u0, bdofsW0_P2_1, W.sub(0))
A = dolfinx.fem.assemble_matrix(a, [bc0, bc1])
A.assemble()
P = dolfinx.fem.assemble_matrix(p_form, [bc0, bc1])
P.assemble()
b = dolfinx.fem.assemble_vector(L)
dolfinx.fem.apply_lifting(b, [a], [[bc0, bc1]])
b.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b, [bc0, bc1])
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A, P)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x = A.createVecRight()
ksp.solve(b, x)
assert ksp.getConvergedReason() > 0
return b.norm(), x.norm(), A.norm(), P.norm()
def test_mixed_element_vector_element_form(cell_type, sign, order):
if cell_type == CellType.triangle or cell_type == CellType.quadrilateral:
mesh = create_unit_square(MPI.COMM_WORLD, 2, 2, cell_type)
else:
mesh = create_unit_cube(MPI.COMM_WORLD, 2, 2, 2, cell_type)
if cell_type == CellType.triangle:
U_el = MixedElement([VectorElement("Lagrange", ufl.triangle, order),
FiniteElement("N1curl", ufl.triangle, order)])
elif cell_type == CellType.quadrilateral:
U_el = MixedElement([VectorElement("Lagrange", ufl.quadrilateral, order),
FiniteElement("RTCE", ufl.quadrilateral, order)])
elif cell_type == CellType.tetrahedron:
U_el = MixedElement([VectorElement("Lagrange", ufl.tetrahedron, order),
FiniteElement("N1curl", ufl.tetrahedron, order)])
elif cell_type == CellType.hexahedron:
U_el = MixedElement([VectorElement("Lagrange", ufl.hexahedron, order),
FiniteElement("NCE", ufl.hexahedron, order)])
U = FunctionSpace(mesh, U_el)
u, p = ufl.TrialFunctions(U)
v, q = ufl.TestFunctions(U)
f = form(inner(u, v) * ufl.dx + inner(p, q)(sign) * ufl.dS)
A = dolfinx.fem.assemble_matrix(f)
A.assemble()
check_symmetry(A)
def monolithic_solve():
"""Monolithic version"""
E = P * P
W = FunctionSpace(mesh, E)
U = Function(W)
dU = ufl.TrialFunction(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
F = inner((u0**2 + 1) * ufl.grad(u0), ufl.grad(v0)) * dx \
+ inner((u1**2 + 1) * ufl.grad(u1), ufl.grad(v1)) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
F, J = form(F), form(J)
u0_bc = Function(V0)
u0_bc.interpolate(bc_val_0)
u1_bc = Function(V1)
u1_bc.interpolate(bc_val_1)
bdofsW0_V0 = locate_dofs_topological((W.sub(0), V0), facetdim,
bndry_facets)
bdofsW1_V1 = locate_dofs_topological((W.sub(1), V1), facetdim,
bndry_facets)
bcs = [
dirichletbc(u0_bc, bdofsW0_V0, W.sub(0)),
dirichletbc(u1_bc, bdofsW1_V1, W.sub(1))
]
Jmat = create_matrix(J)
Fvec = create_vector(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("preonly")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, U, bcs)
snes.setFunction(problem.F_mono, Fvec)
snes.setJacobian(problem.J_mono, J=Jmat, P=None)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
x = create_vector(F)
x.array = U.vector.array_r
snes.solve(None, x)
assert snes.getKSP().getConvergedReason() > 0
assert snes.getConvergedReason() > 0
return x.norm()
def monolithic_assembly(clock, reps, mesh, use_cpp_forms):
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = dolfinx.FunctionSpace(mesh, TH)
num_dofs = W.dim
U = dolfinx.Function(W)
u, p = ufl.split(U)
v, q = ufl.TestFunctions(W)
g = ufl.as_vector([0.0, 0.0, -1.0])
F = (
ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
+ ufl.inner(p, ufl.div(v)) * ufl.dx
+ ufl.inner(ufl.div(u), q) * ufl.dx
- ufl.inner(g, v) * ufl.dx
)
J = ufl.derivative(F, U, ufl.TrialFunction(W))
bcs = []
# Get jitted forms for better performance
if use_cpp_forms:
F = dolfinx.fem.assemble._create_cpp_form(F)
J = dolfinx.fem.assemble._create_cpp_form(J)
b = dolfinx.fem.create_vector(F)
A = dolfinx.fem.create_matrix(J)
for i in range(reps):
A.zeroEntries()
with b.localForm() as b_local:
b_local.set(0.0)
with dolfinx.common.Timer("ZZZ Mat Monolithic") as tmr:
dolfinx.fem.assemble_matrix(A, J, bcs)
A.assemble()
clock["mat"] += tmr.elapsed()[0]
with dolfinx.common.Timer("ZZZ Vec Monolithic") as tmr:
dolfinx.fem.assemble_vector(b, F)
dolfinx.fem.apply_lifting(b, [J], bcs=[bcs], x0=[U.vector], scale=-1.0)
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b, bcs, x0=U.vector, scale=-1.0)
b.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD)
clock["vec"] += tmr.elapsed()[0]
return num_dofs, A, b
def test_matrix_assembly_block(mode):
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic structures.
"""
mesh = UnitSquareMesh(MPI.COMM_WORLD, 4, 8, ghost_mode=mode)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = dolfinx.fem.FunctionSpace(mesh, P0)
V1 = dolfinx.fem.FunctionSpace(mesh, P1)
def boundary(x):
return numpy.logical_or(x[0] < 1.0e-6, x[0] > 1.0 - 1.0e-6)
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = dolfinx.mesh.locate_entities_boundary(
mesh, facetdim, boundary)
bdofsV1 = dolfinx.fem.locate_dofs_topological(V1, facetdim, bndry_facets)
u_bc = dolfinx.fem.Function(V1)
with u_bc.vector.localForm() as u_local:
u_local.set(50.0)
bc = dolfinx.fem.dirichletbc.DirichletBC(u_bc, bdofsV1)
# Define variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = dolfinx.Function(V0)
a00 = inner(u, v) * dx
a01 = inner(p, v) * dx
a10 = inner(u, q) * dx
a11 = inner(p, q) * dx
L0 = zero * inner(f, v) * dx
L1 = inner(g, q) * dx
a_block = [[a00, a01], [a10, a11]]
L_block = [L0, L1]
# Monolithic blocked
A0 = dolfinx.fem.assemble_matrix_block(a_block, [bc])
A0.assemble()
b0 = dolfinx.fem.assemble_vector_block(L_block, a_block, [bc])
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
A1 = dolfinx.fem.assemble_matrix_nest(a_block, [bc],
[["baij", "aij"], ["aij", ""]])
A1.assemble()
Anorm1 = nest_matrix_norm(A1)
assert Anorm0 == pytest.approx(Anorm1, 1.0e-12)
b1 = dolfinx.fem.assemble_vector_nest(L_block)
dolfinx.fem.apply_lifting_nest(b1, a_block, [bc])
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = dolfinx.cpp.fem.bcs_rows(
dolfinx.fem.assemble._create_cpp_form(L_block), [bc])
dolfinx.fem.set_bc_nest(b1, bcs0)
b1.assemble()
bnorm1 = math.sqrt(sum([x.norm()**2 for x in b1.getNestSubVecs()]))
assert bnorm0 == pytest.approx(bnorm1, 1.0e-12)
# Monolithic version
E = P0 * P1
W = dolfinx.fem.FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx + inner(u0, v1) * dx + inner(
u1, v0) * dx
L = zero * inner(f, v0) * ufl.dx + inner(g, v1) * dx
bdofsW_V1 = dolfinx.fem.locate_dofs_topological(
(W.sub(1), V1), mesh.topology.dim - 1, bndry_facets)
bc = dolfinx.fem.dirichletbc.DirichletBC(u_bc, bdofsW_V1, W.sub(1))
A2 = dolfinx.fem.assemble_matrix(a, [bc])
A2.assemble()
b2 = dolfinx.fem.assemble_vector(L)
dolfinx.fem.apply_lifting(b2, [a], [[bc]])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b2, [bc])
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-9)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-9)
def test_assembly_solve_taylor_hood_nl(mesh):
"""Assemble Stokes problem with Taylor-Hood elements and solve."""
gdim = mesh.geometry.dim
P2 = VectorFunctionSpace(mesh, ("Lagrange", 2))
P1 = FunctionSpace(mesh, ("Lagrange", 1))
def boundary0(x):
"""Define boundary x = 0"""
return np.isclose(x[0], 0.0)
def boundary1(x):
"""Define boundary x = 1"""
return np.isclose(x[0], 1.0)
def initial_guess_u(x):
u_init = np.row_stack(
(np.sin(x[0]) * np.sin(x[1]), np.cos(x[0]) * np.cos(x[1])))
if gdim == 3:
u_init = np.row_stack((u_init, np.cos(x[2])))
return u_init
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
u_bc_0 = Function(P2)
u_bc_0.interpolate(
lambda x: np.row_stack(tuple(x[j] + float(j) for j in range(gdim))))
u_bc_1 = Function(P2)
u_bc_1.interpolate(
lambda x: np.row_stack(tuple(np.sin(x[j]) for j in range(gdim))))
facetdim = mesh.topology.dim - 1
bndry_facets0 = locate_entities_boundary(mesh, facetdim, boundary0)
bndry_facets1 = locate_entities_boundary(mesh, facetdim, boundary1)
bdofs0 = locate_dofs_topological(P2, facetdim, bndry_facets0)
bdofs1 = locate_dofs_topological(P2, facetdim, bndry_facets1)
bcs = [dirichletbc(u_bc_0, bdofs0), dirichletbc(u_bc_1, bdofs1)]
u, p = Function(P2), Function(P1)
du, dp = ufl.TrialFunction(P2), ufl.TrialFunction(P1)
v, q = ufl.TestFunction(P2), ufl.TestFunction(P1)
F = [
inner(ufl.grad(u), ufl.grad(v)) * dx + inner(p, ufl.div(v)) * dx,
inner(ufl.div(u), q) * dx
]
J = [[derivative(F[0], u, du),
derivative(F[0], p, dp)],
[derivative(F[1], u, du),
derivative(F[1], p, dp)]]
P = [[J[0][0], None], [None, inner(dp, q) * dx]]
F, J, P = form(F), form(J), form(P)
# -- Blocked and monolithic
Jmat0 = create_matrix_block(J)
Pmat0 = create_matrix_block(P)
Fvec0 = create_vector_block(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("minres")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs, P=P)
snes.setFunction(problem.F_block, Fvec0)
snes.setJacobian(problem.J_block, J=Jmat0, P=Pmat0)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x0 = create_vector_block(F)
with u.vector.localForm() as _u, p.vector.localForm() as _p:
scatter_local_vectors(x0, [_u.array_r, _p.array_r],
[(u.function_space.dofmap.index_map,
u.function_space.dofmap.index_map_bs),
(p.function_space.dofmap.index_map,
p.function_space.dofmap.index_map_bs)])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
snes.solve(None, x0)
assert snes.getConvergedReason() > 0
# -- Blocked and nested
Jmat1 = create_matrix_nest(J)
Pmat1 = create_matrix_nest(P)
Fvec1 = create_vector_nest(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
nested_IS = Jmat1.getNestISs()
snes.getKSP().setType("minres")
snes.getKSP().setTolerances(rtol=1e-12)
snes.getKSP().getPC().setType("fieldsplit")
snes.getKSP().getPC().setFieldSplitIS(["u", nested_IS[0][0]],
["p", nested_IS[1][1]])
ksp_u, ksp_p = snes.getKSP().getPC().getFieldSplitSubKSP()
ksp_u.setType("preonly")
ksp_u.getPC().setType('lu')
ksp_p.setType("preonly")
ksp_p.getPC().setType('lu')
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs, P=P)
snes.setFunction(problem.F_nest, Fvec1)
snes.setJacobian(problem.J_nest, J=Jmat1, P=Pmat1)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x1 = create_vector_nest(F)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
x1.set(0.0)
snes.solve(None, x1)
assert snes.getConvergedReason() > 0
assert nest_matrix_norm(Jmat1) == pytest.approx(Jmat0.norm(), 1.0e-12)
assert Fvec1.norm() == pytest.approx(Fvec0.norm(), 1.0e-12)
assert x1.norm() == pytest.approx(x0.norm(), 1.0e-12)
# -- Monolithic
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = FunctionSpace(mesh, TH)
U = Function(W)
dU = ufl.TrialFunction(W)
u, p = ufl.split(U)
du, dp = ufl.split(dU)
v, q = ufl.TestFunctions(W)
F = inner(ufl.grad(u), ufl.grad(v)) * dx + inner(p, ufl.div(v)) * dx \
+ inner(ufl.div(u), q) * dx
J = derivative(F, U, dU)
P = inner(ufl.grad(du), ufl.grad(v)) * dx + inner(dp, q) * dx
F, J, P = form(F), form(J), form(P)
bdofsW0_P2_0 = locate_dofs_topological((W.sub(0), P2), facetdim,
bndry_facets0)
bdofsW0_P2_1 = locate_dofs_topological((W.sub(0), P2), facetdim,
bndry_facets1)
bcs = [
dirichletbc(u_bc_0, bdofsW0_P2_0, W.sub(0)),
dirichletbc(u_bc_1, bdofsW0_P2_1, W.sub(0))
]
Jmat2 = create_matrix(J)
Pmat2 = create_matrix(P)
Fvec2 = create_vector(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("minres")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, U, bcs, P=P)
snes.setFunction(problem.F_mono, Fvec2)
snes.setJacobian(problem.J_mono, J=Jmat2, P=Pmat2)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
x2 = create_vector(F)
x2.array = U.vector.array_r
snes.solve(None, x2)
assert snes.getConvergedReason() > 0
assert Jmat2.norm() == pytest.approx(Jmat0.norm(), 1.0e-12)
assert Fvec2.norm() == pytest.approx(Fvec0.norm(), 1.0e-12)
assert x2.norm() == pytest.approx(x0.norm(), 1.0e-12)
def test_matrix_assembly_block_nl():
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic structures
in the nonlinear setting
"""
mesh = create_unit_square(MPI.COMM_WORLD, 4, 8)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = FunctionSpace(mesh, P0)
V1 = FunctionSpace(mesh, P1)
def initial_guess_u(x):
return np.sin(x[0]) * np.sin(x[1])
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
def bc_value(x):
return np.cos(x[0]) * np.cos(x[1])
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(
mesh, facetdim,
lambda x: np.logical_or(np.isclose(x[0], 0.0), np.isclose(x[0], 1.0)))
u_bc = Function(V1)
u_bc.interpolate(bc_value)
bdofs = locate_dofs_topological(V1, facetdim, bndry_facets)
bc = dirichletbc(u_bc, bdofs)
# Define variational problem
du, dp = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
u, p = Function(V0), Function(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
f = 1.0
g = -3.0
F0 = inner(u, v) * dx + inner(p, v) * dx - inner(f, v) * dx
F1 = inner(u, q) * dx + inner(p, q) * dx - inner(g, q) * dx
a_block = form([[derivative(F0, u, du),
derivative(F0, p, dp)],
[derivative(F1, u, du),
derivative(F1, p, dp)]])
L_block = form([F0, F1])
# Monolithic blocked
x0 = create_vector_block(L_block)
scatter_local_vectors(x0, [u.vector.array_r, p.vector.array_r],
[(u.function_space.dofmap.index_map,
u.function_space.dofmap.index_map_bs),
(p.function_space.dofmap.index_map,
p.function_space.dofmap.index_map_bs)])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Ghosts are updated inside assemble_vector_block
A0 = assemble_matrix_block(a_block, bcs=[bc])
b0 = assemble_vector_block(L_block, a_block, bcs=[bc], x0=x0, scale=-1.0)
A0.assemble()
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
x1 = create_vector_nest(L_block)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
A1 = assemble_matrix_nest(a_block, bcs=[bc])
b1 = assemble_vector_nest(L_block)
apply_lifting_nest(b1, a_block, bcs=[bc], x0=x1, scale=-1.0)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L.function_spaces[0] for L in L_block], [bc])
set_bc_nest(b1, bcs0, x1, scale=-1.0)
A1.assemble()
assert A1.getType() == "nest"
assert nest_matrix_norm(A1) == pytest.approx(Anorm0, 1.0e-12)
assert b1.norm() == pytest.approx(bnorm0, 1.0e-12)
# Monolithic version
E = P0 * P1
W = FunctionSpace(mesh, E)
dU = ufl.TrialFunction(W)
U = Function(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
F = inner(u0, v0) * dx + inner(u1, v0) * dx + inner(u0, v1) * dx + inner(u1, v1) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
F, J = form(F), form(J)
bdofsW_V1 = locate_dofs_topological((W.sub(1), V1), facetdim, bndry_facets)
bc = dirichletbc(u_bc, bdofsW_V1, W.sub(1))
A2 = assemble_matrix(J, bcs=[bc])
A2.assemble()
b2 = assemble_vector(F)
apply_lifting(b2, [J], bcs=[[bc]], x0=[U.vector], scale=-1.0)
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, [bc], x0=U.vector, scale=-1.0)
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-12)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-12)
def test_matrix_assembly_block():
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic structures
in the nonlinear setting
"""
mesh = dolfinx.generation.UnitSquareMesh(dolfinx.MPI.comm_world, 4, 8)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = dolfinx.function.FunctionSpace(mesh, P0)
V1 = dolfinx.function.FunctionSpace(mesh, P1)
def boundary(x):
return numpy.logical_or(x[0] < 1.0e-6, x[0] > 1.0 - 1.0e-6)
def initial_guess_u(x):
return numpy.sin(x[0]) * numpy.sin(x[1])
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
def bc_value(x):
return numpy.cos(x[0]) * numpy.cos(x[1])
facetdim = mesh.topology.dim - 1
mf = dolfinx.MeshFunction("size_t", mesh, facetdim, 0)
mf.mark(boundary, 1)
bndry_facets = numpy.where(mf.values == 1)[0]
u_bc = dolfinx.function.Function(V1)
u_bc.interpolate(bc_value)
bdofs = dolfinx.fem.locate_dofs_topological(V1, facetdim, bndry_facets)
bc = dolfinx.fem.dirichletbc.DirichletBC(u_bc, bdofs)
# Define variational problem
du, dp = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
u, p = dolfinx.function.Function(V0), dolfinx.function.Function(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
f = 1.0
g = -3.0
F0 = inner(u, v) * dx + inner(p, v) * dx - inner(f, v) * dx
F1 = inner(u, q) * dx + inner(p, q) * dx - inner(g, q) * dx
a_block = [[derivative(F0, u, du),
derivative(F0, p, dp)],
[derivative(F1, u, du),
derivative(F1, p, dp)]]
L_block = [F0, F1]
# Monolithic blocked
x0 = dolfinx.fem.create_vector_block(L_block)
dolfinx.cpp.la.scatter_local_vectors(
x0, [u.vector.array_r, p.vector.array_r],
[u.function_space.dofmap.index_map, p.function_space.dofmap.index_map])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Ghosts are updated inside assemble_vector_block
A0 = dolfinx.fem.assemble_matrix_block(a_block, [bc])
b0 = dolfinx.fem.assemble_vector_block(L_block,
a_block, [bc],
x0=x0,
scale=-1.0)
A0.assemble()
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
x1 = dolfinx.fem.create_vector_nest(L_block)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
A1 = dolfinx.fem.assemble_matrix_nest(a_block, [bc])
b1 = dolfinx.fem.assemble_vector_nest(L_block)
dolfinx.fem.apply_lifting_nest(b1, a_block, [bc], x1, scale=-1.0)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = dolfinx.cpp.fem.bcs_rows(
dolfinx.fem.assemble._create_cpp_form(L_block), [bc])
dolfinx.fem.set_bc_nest(b1, bcs0, x1, scale=-1.0)
A1.assemble()
assert A1.getType() == "nest"
assert nest_matrix_norm(A1) == pytest.approx(Anorm0, 1.0e-12)
assert b1.norm() == pytest.approx(bnorm0, 1.0e-12)
# Monolithic version
E = P0 * P1
W = dolfinx.function.FunctionSpace(mesh, E)
dU = ufl.TrialFunction(W)
U = dolfinx.function.Function(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
U.interpolate(lambda x: numpy.row_stack(
(initial_guess_u(x), initial_guess_p(x))))
F = inner(u0, v0) * dx + inner(u1, v0) * dx + inner(u0, v1) * dx + inner(u1, v1) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
bdofsW_V1 = dolfinx.fem.locate_dofs_topological((W.sub(1), V1), facetdim,
bndry_facets)
bc = dolfinx.fem.dirichletbc.DirichletBC(u_bc, bdofsW_V1, W.sub(1))
A2 = dolfinx.fem.assemble_matrix(J, [bc])
A2.assemble()
b2 = dolfinx.fem.assemble_vector(F)
dolfinx.fem.apply_lifting(b2, [J], bcs=[[bc]], x0=[U.vector], scale=-1.0)
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b2, [bc], x0=U.vector, scale=-1.0)
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-12)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-12)
def test_assembly_solve_block():
"""Solve a two-field nonlinear diffusion like problem with block matrix
approaches and test that solution is the same.
"""
mesh = dolfinx.generation.UnitSquareMesh(dolfinx.MPI.comm_world, 12, 11)
p = 1
P = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p)
V0 = dolfinx.function.FunctionSpace(mesh, P)
V1 = V0.clone()
def bc_val_0(x):
return x[0]**2 + x[1]**2
def bc_val_1(x):
return numpy.sin(x[0]) * numpy.cos(x[1])
def initial_guess_u(x):
return numpy.sin(x[0]) * numpy.sin(x[1])
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
def boundary(x):
return numpy.logical_or(x[0] < 1.0e-6, x[0] > 1.0 - 1.0e-6)
facetdim = mesh.topology.dim - 1
mf = dolfinx.MeshFunction("size_t", mesh, facetdim, 0)
mf.mark(boundary, 1)
bndry_facets = numpy.where(mf.values == 1)[0]
u_bc0 = dolfinx.function.Function(V0)
u_bc0.interpolate(bc_val_0)
u_bc1 = dolfinx.function.Function(V1)
u_bc1.interpolate(bc_val_1)
bdofs0 = dolfinx.fem.locate_dofs_topological(V0, facetdim, bndry_facets)
bdofs1 = dolfinx.fem.locate_dofs_topological(V1, facetdim, bndry_facets)
bcs = [
dolfinx.fem.dirichletbc.DirichletBC(u_bc0, bdofs0),
dolfinx.fem.dirichletbc.DirichletBC(u_bc1, bdofs1)
]
# Block and Nest variational problem
u, p = dolfinx.function.Function(V0), dolfinx.function.Function(V1)
du, dp = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
F = [
inner((u**2 + 1) * ufl.grad(u), ufl.grad(v)) * dx - inner(f, v) * dx,
inner((p**2 + 1) * ufl.grad(p), ufl.grad(q)) * dx - inner(g, q) * dx
]
J = [[derivative(F[0], u, du),
derivative(F[0], p, dp)],
[derivative(F[1], u, du),
derivative(F[1], p, dp)]]
# -- Blocked version
Jmat0 = dolfinx.fem.create_matrix_block(J)
Fvec0 = dolfinx.fem.create_vector_block(F)
snes = PETSc.SNES().create(dolfinx.MPI.comm_world)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("preonly")
snes.getKSP().getPC().setType("lu")
snes.getKSP().getPC().setFactorSolverType("mumps")
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs)
snes.setFunction(problem.F_block, Fvec0)
snes.setJacobian(problem.J_block, J=Jmat0, P=None)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x0 = dolfinx.fem.create_vector_block(F)
dolfinx.cpp.la.scatter_local_vectors(
x0, [u.vector.array_r, p.vector.array_r],
[u.function_space.dofmap.index_map, p.function_space.dofmap.index_map])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
snes.solve(None, x0)
assert snes.getKSP().getConvergedReason() > 0
assert snes.getConvergedReason() > 0
J0norm = Jmat0.norm()
F0norm = Fvec0.norm()
x0norm = x0.norm()
# -- Nested (MatNest)
Jmat1 = dolfinx.fem.create_matrix_nest(J)
Fvec1 = dolfinx.fem.create_vector_nest(F)
snes = PETSc.SNES().create(dolfinx.MPI.comm_world)
snes.setTolerances(rtol=1.0e-15, max_it=10)
nested_IS = Jmat1.getNestISs()
snes.getKSP().setType("fgmres")
snes.getKSP().setTolerances(rtol=1e-12)
snes.getKSP().getPC().setType("fieldsplit")
snes.getKSP().getPC().setFieldSplitIS(["u", nested_IS[0][0]],
["p", nested_IS[1][1]])
ksp_u, ksp_p = snes.getKSP().getPC().getFieldSplitSubKSP()
ksp_u.setType("preonly")
ksp_u.getPC().setType('lu')
ksp_u.getPC().setFactorSolverType('mumps')
ksp_p.setType("preonly")
ksp_p.getPC().setType('lu')
ksp_p.getPC().setFactorSolverType('mumps')
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs)
snes.setFunction(problem.F_nest, Fvec1)
snes.setJacobian(problem.J_nest, J=Jmat1, P=None)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x1 = dolfinx.fem.create_vector_nest(F)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
snes.solve(None, x1)
assert snes.getKSP().getConvergedReason() > 0
assert snes.getConvergedReason() > 0
assert x1.getType() == "nest"
assert Jmat1.getType() == "nest"
assert Fvec1.getType() == "nest"
J1norm = nest_matrix_norm(Jmat1)
F1norm = Fvec1.norm()
x1norm = x1.norm()
assert J1norm == pytest.approx(J0norm, 1.0e-12)
assert F1norm == pytest.approx(F0norm, 1.0e-12)
assert x1norm == pytest.approx(x0norm, 1.0e-12)
# -- Monolithic version
E = P * P
W = dolfinx.function.FunctionSpace(mesh, E)
U = dolfinx.function.Function(W)
dU = ufl.TrialFunction(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
F = inner((u0**2 + 1) * ufl.grad(u0), ufl.grad(v0)) * dx \
+ inner((u1**2 + 1) * ufl.grad(u1), ufl.grad(v1)) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
u0_bc = dolfinx.function.Function(V0)
u0_bc.interpolate(bc_val_0)
u1_bc = dolfinx.function.Function(V1)
u1_bc.interpolate(bc_val_1)
bdofsW0_V0 = dolfinx.fem.locate_dofs_topological((W.sub(0), V0), facetdim,
bndry_facets)
bdofsW1_V1 = dolfinx.fem.locate_dofs_topological((W.sub(1), V1), facetdim,
bndry_facets)
bcs = [
dolfinx.fem.dirichletbc.DirichletBC(u0_bc, bdofsW0_V0, W.sub(0)),
dolfinx.fem.dirichletbc.DirichletBC(u1_bc, bdofsW1_V1, W.sub(1))
]
Jmat2 = dolfinx.fem.create_matrix(J)
Fvec2 = dolfinx.fem.create_vector(F)
snes = PETSc.SNES().create(dolfinx.MPI.comm_world)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("preonly")
snes.getKSP().getPC().setType("lu")
snes.getKSP().getPC().setFactorSolverType("mumps")
problem = NonlinearPDE_SNESProblem(F, J, U, bcs)
snes.setFunction(problem.F_mono, Fvec2)
snes.setJacobian(problem.J_mono, J=Jmat2, P=None)
U.interpolate(lambda x: numpy.row_stack(
(initial_guess_u(x), initial_guess_p(x))))
x2 = dolfinx.fem.create_vector(F)
x2.array = U.vector.array_r
snes.solve(None, x2)
assert snes.getKSP().getConvergedReason() > 0
assert snes.getConvergedReason() > 0
J2norm = Jmat2.norm()
F2norm = Fvec2.norm()
x2norm = x2.norm()
assert J2norm == pytest.approx(J0norm, 1.0e-12)
assert F2norm == pytest.approx(F0norm, 1.0e-12)
assert x2norm == pytest.approx(x0norm, 1.0e-12)
def test_mixed_element(cell_type, ghost_mode):
N = 4
mesh = dolfinx.mesh.create_unit_square(
MPI.COMM_WORLD, N, N, cell_type=cell_type, ghost_mode=ghost_mode)
# Inlet velocity Dirichlet BC
bc_facets = dolfinx.mesh.locate_entities_boundary(
mesh, mesh.topology.dim - 1, lambda x: np.isclose(x[0], 0.0))
other_facets = dolfinx.mesh.locate_entities_boundary(
mesh, mesh.topology.dim - 1, lambda x: np.isclose(x[0], 1.0))
arg_sort = np.argsort(other_facets)
mt = dolfinx.mesh.meshtags(mesh, mesh.topology.dim - 1,
other_facets[arg_sort], np.full_like(other_facets, 1))
# Rotate the mesh to induce more interesting slip BCs
th = np.pi / 4.0
rot = np.array([[np.cos(th), -np.sin(th)],
[np.sin(th), np.cos(th)]])
gdim = mesh.geometry.dim
mesh.geometry.x[:, :gdim] = (rot @ mesh.geometry.x[:, :gdim].T).T
# Create the function space
Ve = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
Qe = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
V = dolfinx.fem.FunctionSpace(mesh, Ve)
Q = dolfinx.fem.FunctionSpace(mesh, Qe)
W = dolfinx.fem.FunctionSpace(mesh, Ve * Qe)
inlet_velocity = dolfinx.fem.Function(V)
inlet_velocity.interpolate(
lambda x: np.zeros((mesh.geometry.dim, x[0].shape[0]), dtype=np.double))
inlet_velocity.x.scatter_forward()
# -- Nested assembly
dofs = dolfinx.fem.locate_dofs_topological(V, 1, bc_facets)
bc1 = dolfinx.fem.dirichletbc(inlet_velocity, dofs)
# Collect Dirichlet boundary conditions
bcs = [bc1]
mpc_v = dolfinx_mpc.MultiPointConstraint(V)
n_approx = dolfinx_mpc.utils.create_normal_approximation(V, mt, 1)
mpc_v.create_slip_constraint(V, (mt, 1), n_approx, bcs=bcs)
mpc_v.finalize()
mpc_q = dolfinx_mpc.MultiPointConstraint(Q)
mpc_q.finalize()
f = dolfinx.fem.Constant(mesh, PETSc.ScalarType((0, 0)))
(u, p) = ufl.TrialFunction(V), ufl.TrialFunction(Q)
(v, q) = ufl.TestFunction(V), ufl.TestFunction(Q)
a00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
a01 = - ufl.inner(p, ufl.div(v)) * ufl.dx
a10 = - ufl.inner(ufl.div(u), q) * ufl.dx
a11 = None
L0 = ufl.inner(f, v) * ufl.dx
L1 = ufl.inner(
dolfinx.fem.Constant(mesh, PETSc.ScalarType(0.0)), q) * ufl.dx
n = ufl.FacetNormal(mesh)
g_tau = ufl.as_vector((0.0, 0.0))
ds = ufl.Measure("ds", domain=mesh, subdomain_data=mt, subdomain_id=1)
a00 -= ufl.inner(ufl.outer(n, n) * ufl.dot(ufl.grad(u), n), v) * ds
a01 -= ufl.inner(ufl.outer(n, n) * ufl.dot(
- p * ufl.Identity(u.ufl_shape[0]), n), v) * ds
L0 += ufl.inner(g_tau, v) * ds
a_nest = dolfinx.fem.form(((a00, a01),
(a10, a11)))
L_nest = dolfinx.fem.form((L0, L1))
# Assemble MPC nest matrix
A_nest = dolfinx_mpc.create_matrix_nest(a_nest, [mpc_v, mpc_q])
dolfinx_mpc.assemble_matrix_nest(A_nest, a_nest, [mpc_v, mpc_q], bcs)
A_nest.assemble()
# Assemble original nest matrix
A_org_nest = dolfinx.fem.petsc.assemble_matrix_nest(a_nest, bcs)
A_org_nest.assemble()
# MPC nested rhs
b_nest = dolfinx_mpc.create_vector_nest(L_nest, [mpc_v, mpc_q])
dolfinx_mpc.assemble_vector_nest(b_nest, L_nest, [mpc_v, mpc_q])
dolfinx.fem.petsc.apply_lifting_nest(b_nest, a_nest, bcs)
for b_sub in b_nest.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = dolfinx.fem.bcs_by_block(
dolfinx.fem.extract_function_spaces(L_nest), bcs)
dolfinx.fem.petsc.set_bc_nest(b_nest, bcs0)
# Original dolfinx rhs
b_org_nest = dolfinx.fem.petsc.assemble_vector_nest(L_nest)
dolfinx.fem.petsc.apply_lifting_nest(b_org_nest, a_nest, bcs)
for b_sub in b_org_nest.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.petsc.set_bc_nest(b_org_nest, bcs0)
# -- Monolithic assembly
dofs = dolfinx.fem.locate_dofs_topological((W.sub(0), V), 1, bc_facets)
bc1 = dolfinx.fem.dirichletbc(inlet_velocity, dofs, W.sub(0))
bcs = [bc1]
V, V_to_W = W.sub(0).collapse()
mpc_vq = dolfinx_mpc.MultiPointConstraint(W)
n_approx = dolfinx_mpc.utils.create_normal_approximation(V, mt, 1)
mpc_vq.create_slip_constraint(W.sub(0), (mt, 1), n_approx, bcs=bcs)
mpc_vq.finalize()
f = dolfinx.fem.Constant(mesh, PETSc.ScalarType((0, 0)))
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
a = (
ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
- ufl.inner(p, ufl.div(v)) * ufl.dx
- ufl.inner(ufl.div(u), q) * ufl.dx
)
L = ufl.inner(f, v) * ufl.dx + ufl.inner(
dolfinx.fem.Constant(mesh, PETSc.ScalarType(0.0)), q) * ufl.dx
# No prescribed shear stress
n = ufl.FacetNormal(mesh)
g_tau = ufl.as_vector((0.0, 0.0))
ds = ufl.Measure("ds", domain=mesh, subdomain_data=mt, subdomain_id=1)
# Terms due to slip condition
# Explained in for instance: https://arxiv.org/pdf/2001.10639.pdf
a -= ufl.inner(ufl.outer(n, n) * ufl.dot(ufl.grad(u), n), v) * ds
a -= ufl.inner(ufl.outer(n, n) * ufl.dot(
- p * ufl.Identity(u.ufl_shape[0]), n), v) * ds
L += ufl.inner(g_tau, v) * ds
a, L = dolfinx.fem.form(a), dolfinx.fem.form(L)
# Assemble LHS matrix and RHS vector
A = dolfinx_mpc.assemble_matrix(a, mpc_vq, bcs)
A.assemble()
A_org = dolfinx.fem.petsc.assemble_matrix(a, bcs)
A_org.assemble()
b = dolfinx_mpc.assemble_vector(L, mpc_vq)
b_org = dolfinx.fem.petsc.assemble_vector(L)
# Set Dirichlet boundary condition values in the RHS
dolfinx_mpc.apply_lifting(b, [a], [bcs], mpc_vq)
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.petsc.set_bc(b, bcs)
dolfinx.fem.petsc.apply_lifting(b_org, [a], [bcs])
b_org.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.petsc.set_bc(b_org, bcs)
# -- Verification
def nest_matrix_norm(A):
assert A.getType() == "nest"
nrows, ncols = A.getNestSize()
sub_A = [A.getNestSubMatrix(row, col)
for row in range(nrows) for col in range(ncols)]
return sum(map(lambda A_: A_.norm()**2 if A_ else 0.0, sub_A))**0.5
# -- Ensure monolithic and nest matrices are the same
assert np.isclose(nest_matrix_norm(A_nest), A.norm())
def test_assembly_solve_taylor_hood(mesh):
"""Assemble Stokes problem with Taylor-Hood elements and solve."""
P2 = function.VectorFunctionSpace(mesh, ("Lagrange", 2))
P1 = function.FunctionSpace(mesh, ("Lagrange", 1))
def boundary0(x):
"""Define boundary x = 0"""
return x[0] < 10 * numpy.finfo(float).eps
def boundary1(x):
"""Define boundary x = 1"""
return x[0] > (1.0 - 10 * numpy.finfo(float).eps)
facetdim = mesh.topology.dim - 1
mf = dolfinx.MeshFunction("size_t", mesh, facetdim, 0)
mf.mark(boundary0, 1)
mf.mark(boundary1, 2)
bndry_facets0 = numpy.where(mf.values == 1)[0]
bndry_facets1 = numpy.where(mf.values == 2)[0]
bdofs0 = dolfinx.fem.locate_dofs_topological(P2, facetdim, bndry_facets0)
bdofs1 = dolfinx.fem.locate_dofs_topological(P2, facetdim, bndry_facets1)
u0 = dolfinx.Function(P2)
u0.vector.set(1.0)
u0.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
bc0 = dolfinx.DirichletBC(u0, bdofs0)
bc1 = dolfinx.DirichletBC(u0, bdofs1)
u, p = ufl.TrialFunction(P2), ufl.TrialFunction(P1)
v, q = ufl.TestFunction(P2), ufl.TestFunction(P1)
a00 = inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a11 = None
p00 = a00
p01, p10 = None, None
p11 = inner(p, q) * dx
# FIXME
# We need zero function for the 'zero' part of L
p_zero = dolfinx.Function(P1)
f = dolfinx.Function(P2)
L0 = ufl.inner(f, v) * dx
L1 = ufl.inner(p_zero, q) * dx
# -- Blocked (nested)
A0 = dolfinx.fem.assemble_matrix_nest([[a00, a01], [a10, a11]], [bc0, bc1])
A0.assemble()
A0norm = nest_matrix_norm(A0)
P0 = dolfinx.fem.assemble_matrix_nest([[p00, p01], [p10, p11]], [bc0, bc1])
P0.assemble()
P0norm = nest_matrix_norm(P0)
b0 = dolfinx.fem.assemble_vector_nest([L0, L1])
dolfinx.fem.apply_lifting_nest(b0, [[a00, a01], [a10, a11]], [bc0, bc1])
for b_sub in b0.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = dolfinx.cpp.fem.bcs_rows(
dolfinx.fem.assemble._create_cpp_form([L0, L1]), [bc0, bc1])
dolfinx.fem.set_bc_nest(b0, bcs0)
b0.assemble()
b0norm = b0.norm()
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A0, P0)
nested_IS = P0.getNestISs()
ksp.setType("minres")
pc = ksp.getPC()
pc.setType("fieldsplit")
pc.setFieldSplitIS(["u", nested_IS[0][0]], ["p", nested_IS[1][1]])
ksp_u, ksp_p = pc.getFieldSplitSubKSP()
ksp_u.setType("preonly")
ksp_u.getPC().setType('lu')
ksp_u.getPC().setFactorSolverType('mumps')
ksp_p.setType("preonly")
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x0 = b0.copy()
ksp.solve(b0, x0)
assert ksp.getConvergedReason() > 0
# -- Blocked (monolithic)
A1 = dolfinx.fem.assemble_matrix_block([[a00, a01], [a10, a11]],
[bc0, bc1])
A1.assemble()
assert A1.norm() == pytest.approx(A0norm, 1.0e-12)
P1 = dolfinx.fem.assemble_matrix_block([[p00, p01], [p10, p11]],
[bc0, bc1])
P1.assemble()
assert P1.norm() == pytest.approx(P0norm, 1.0e-12)
b1 = dolfinx.fem.assemble_vector_block([L0, L1], [[a00, a01], [a10, a11]],
[bc0, bc1])
assert b1.norm() == pytest.approx(b0norm, 1.0e-12)
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A1, P1)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
pc.setFactorSolverType('mumps')
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setFromOptions()
x1 = A1.createVecRight()
ksp.solve(b1, x1)
assert ksp.getConvergedReason() > 0
assert x1.norm() == pytest.approx(x0.norm(), 1e-8)
# -- Monolithic
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = dolfinx.FunctionSpace(mesh, TH)
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
a00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a = a00 + a01 + a10
p00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
p11 = ufl.inner(p, q) * dx
p_form = p00 + p11
f = dolfinx.Function(W.sub(0).collapse())
p_zero = dolfinx.Function(W.sub(1).collapse())
L0 = inner(f, v) * dx
L1 = inner(p_zero, q) * dx
L = L0 + L1
bdofsW0_P2_0 = dolfinx.fem.locate_dofs_topological((W.sub(0), P2),
facetdim, bndry_facets0)
bdofsW0_P2_1 = dolfinx.fem.locate_dofs_topological((W.sub(0), P2),
facetdim, bndry_facets1)
bc0 = dolfinx.DirichletBC(u0, bdofsW0_P2_0, W.sub(0))
bc1 = dolfinx.DirichletBC(u0, bdofsW0_P2_1, W.sub(0))
A2 = dolfinx.fem.assemble_matrix(a, [bc0, bc1])
A2.assemble()
assert A2.norm() == pytest.approx(A0norm, 1.0e-12)
P2 = dolfinx.fem.assemble_matrix(p_form, [bc0, bc1])
P2.assemble()
assert P2.norm() == pytest.approx(P0norm, 1.0e-12)
b2 = dolfinx.fem.assemble_vector(L)
dolfinx.fem.apply_lifting(b2, [a], [[bc0, bc1]])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b2, [bc0, bc1])
b2norm = b2.norm()
assert b2norm == pytest.approx(b0norm, 1.0e-12)
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A2, P2)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
pc.setFactorSolverType('mumps')
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x2 = A2.createVecRight()
ksp.solve(b2, x2)
assert ksp.getConvergedReason() > 0
assert x0.norm() == pytest.approx(x2.norm(), 1e-8)
def test_biharmonic():
"""Manufactured biharmonic problem.
Solved using rotated Regge mixed finite element method. This is equivalent
to the Hellan-Herrmann-Johnson (HHJ) finite element method in
two-dimensions."""
mesh = RectangleMesh(MPI.COMM_WORLD, [np.array([0.0, 0.0, 0.0]),
np.array([1.0, 1.0, 0.0])], [32, 32], CellType.triangle)
element = ufl.MixedElement([ufl.FiniteElement("Regge", ufl.triangle, 1),
ufl.FiniteElement("Lagrange", ufl.triangle, 2)])
V = FunctionSpace(mesh, element)
sigma, u = ufl.TrialFunctions(V)
tau, v = ufl.TestFunctions(V)
x = ufl.SpatialCoordinate(mesh)
u_exact = ufl.sin(ufl.pi * x[0]) * ufl.sin(ufl.pi * x[0]) * ufl.sin(ufl.pi * x[1]) * ufl.sin(ufl.pi * x[1])
f_exact = div(grad(div(grad(u_exact))))
sigma_exact = grad(grad(u_exact))
# sigma and tau are tangential-tangential continuous according to the
# H(curl curl) continuity of the Regge space. However, for the biharmonic
# problem we require normal-normal continuity H (div div). Theorem 4.2 of
# Lizao Li's PhD thesis shows that the latter space can be constructed by
# the former through the action of the operator S:
def S(tau):
return tau - ufl.Identity(2) * ufl.tr(tau)
sigma_S = S(sigma)
tau_S = S(tau)
# Discrete duality inner product eq. 4.5 Lizao Li's PhD thesis
def b(tau_S, v):
n = FacetNormal(mesh)
return inner(tau_S, grad(grad(v))) * dx \
- ufl.dot(ufl.dot(tau_S('+'), n('+')), n('+')) * jump(grad(v), n) * dS \
- ufl.dot(ufl.dot(tau_S, n), n) * ufl.dot(grad(v), n) * ds
# Non-symmetric formulation
a = inner(sigma_S, tau_S) * dx - b(tau_S, u) + b(sigma_S, v)
L = inner(f_exact, v) * dx
V_1 = V.sub(1).collapse()
zero_u = Function(V_1)
with zero_u.vector.localForm() as zero_u_local:
zero_u_local.set(0.0)
# Strong (Dirichlet) boundary condition
boundary_facets = locate_entities_boundary(
mesh, mesh.topology.dim - 1, lambda x: np.full(x.shape[1], True, dtype=bool))
boundary_dofs = locate_dofs_topological((V.sub(1), V_1), mesh.topology.dim - 1, boundary_facets)
bcs = [DirichletBC(zero_u, boundary_dofs, V.sub(1))]
A = assemble_matrix(a, bcs=bcs)
A.assemble()
b = assemble_vector(L)
apply_lifting(b, [a], [bcs])
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
# Solve
solver = PETSc.KSP().create(MPI.COMM_WORLD)
PETSc.Options()["ksp_type"] = "preonly"
PETSc.Options()["pc_type"] = "lu"
# PETSc.Options()["pc_factor_mat_solver_type"] = "mumps"
solver.setFromOptions()
solver.setOperators(A)
x_h = Function(V)
solver.solve(b, x_h.vector)
x_h.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Recall that x_h has flattened indices.
u_error_numerator = np.sqrt(mesh.mpi_comm().allreduce(assemble_scalar(
inner(u_exact - x_h[4], u_exact - x_h[4]) * dx(mesh, metadata={"quadrature_degree": 5})), op=MPI.SUM))
u_error_denominator = np.sqrt(mesh.mpi_comm().allreduce(assemble_scalar(
inner(u_exact, u_exact) * dx(mesh, metadata={"quadrature_degree": 5})), op=MPI.SUM))
assert(np.absolute(u_error_numerator / u_error_denominator) < 0.05)
# Reconstruct tensor from flattened indices.
# Apply inverse transform. In 2D we have S^{-1} = S.
sigma_h = S(ufl.as_tensor([[x_h[0], x_h[1]], [x_h[2], x_h[3]]]))
sigma_error_numerator = np.sqrt(mesh.mpi_comm().allreduce(assemble_scalar(
inner(sigma_exact - sigma_h, sigma_exact - sigma_h) * dx(mesh, metadata={"quadrature_degree": 5})), op=MPI.SUM))
sigma_error_denominator = np.sqrt(mesh.mpi_comm().allreduce(assemble_scalar(
inner(sigma_exact, sigma_exact) * dx(mesh, metadata={"quadrature_degree": 5})), op=MPI.SUM))
assert(np.absolute(sigma_error_numerator / sigma_error_denominator) < 0.005)
def test_assembly_solve_block(mode):
"""Solve a two-field mass-matrix like problem with block matrix approaches
and test that solution is the same"""
mesh = create_unit_square(MPI.COMM_WORLD, 32, 31, ghost_mode=mode)
P = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
V0 = FunctionSpace(mesh, P)
V1 = V0.clone()
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(mesh, facetdim, lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 1.0)))
bdofsV0 = locate_dofs_topological(V0, facetdim, bndry_facets)
bdofsV1 = locate_dofs_topological(V1, facetdim, bndry_facets)
u0_bc = PETSc.ScalarType(50.0)
u1_bc = PETSc.ScalarType(20.0)
bcs = [dirichletbc(u0_bc, bdofsV0, V0), dirichletbc(u1_bc, bdofsV1, V1)]
# Variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = Function(V0)
a00 = form(inner(u, v) * dx)
a01 = form(zero * inner(p, v) * dx)
a10 = form(zero * inner(u, q) * dx)
a11 = form(inner(p, q) * dx)
L0 = form(inner(f, v) * dx)
L1 = form(inner(g, q) * dx)
def monitor(ksp, its, rnorm):
pass
# print("Norm:", its, rnorm)
A0 = assemble_matrix_block([[a00, a01], [a10, a11]], bcs=bcs)
b0 = assemble_vector_block([L0, L1], [[a00, a01], [a10, a11]], bcs=bcs)
A0.assemble()
A0norm = A0.norm()
b0norm = b0.norm()
x0 = A0.createVecLeft()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setOperators(A0)
ksp.setMonitor(monitor)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-14)
ksp.setFromOptions()
ksp.solve(b0, x0)
x0norm = x0.norm()
# Nested (MatNest)
A1 = assemble_matrix_nest([[a00, a01], [a10, a11]], bcs=bcs, diagonal=1.0)
A1.assemble()
b1 = assemble_vector_nest([L0, L1])
apply_lifting_nest(b1, [[a00, a01], [a10, a11]], bcs=bcs)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L0.function_spaces[0], L1.function_spaces[0]], bcs)
set_bc_nest(b1, bcs0)
b1.assemble()
b1norm = b1.norm()
assert b1norm == pytest.approx(b0norm, 1.0e-12)
A1norm = nest_matrix_norm(A1)
assert A0norm == pytest.approx(A1norm, 1.0e-12)
x1 = b1.copy()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setMonitor(monitor)
ksp.setOperators(A1)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b1, x1)
x1norm = x1.norm()
assert x1norm == pytest.approx(x0norm, rel=1.0e-12)
# Monolithic version
E = P * P
W = FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx
L = inner(f, v0) * ufl.dx + inner(g, v1) * dx
a, L = form(a), form(L)
bdofsW0_V0 = locate_dofs_topological(W.sub(0), facetdim, bndry_facets)
bdofsW1_V1 = locate_dofs_topological(W.sub(1), facetdim, bndry_facets)
bcs = [dirichletbc(u0_bc, bdofsW0_V0, W.sub(0)), dirichletbc(u1_bc, bdofsW1_V1, W.sub(1))]
A2 = assemble_matrix(a, bcs=bcs)
A2.assemble()
b2 = assemble_vector(L)
apply_lifting(b2, [a], [bcs])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, bcs)
A2norm = A2.norm()
b2norm = b2.norm()
assert A2norm == pytest.approx(A0norm, 1.0e-12)
assert b2norm == pytest.approx(b0norm, 1.0e-12)
x2 = b2.copy()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setMonitor(monitor)
ksp.setOperators(A2)
ksp.setType('cg')
ksp.getPC().setType('jacobi')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b2, x2)
x2norm = x2.norm()
assert x2norm == pytest.approx(x0norm, 1.0e-10)
def test_matrix_assembly_block(mode):
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic
structures"""
mesh = create_unit_square(MPI.COMM_WORLD, 4, 8, ghost_mode=mode)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = FunctionSpace(mesh, P0)
V1 = FunctionSpace(mesh, P1)
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(mesh, facetdim, lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 1.0)))
bdofsV1 = locate_dofs_topological(V1, facetdim, bndry_facets)
u_bc = PETSc.ScalarType(50.0)
bc = dirichletbc(u_bc, bdofsV1, V1)
# Define variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = Function(V0)
a00 = inner(u, v) * dx
a01 = inner(p, v) * dx
a10 = inner(u, q) * dx
a11 = inner(p, q) * dx
L0 = zero * inner(f, v) * dx
L1 = inner(g, q) * dx
a_block = form([[a00, a01], [a10, a11]])
L_block = form([L0, L1])
# Monolithic blocked
A0 = assemble_matrix_block(a_block, bcs=[bc])
A0.assemble()
b0 = assemble_vector_block(L_block, a_block, bcs=[bc])
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
A1 = assemble_matrix_nest(a_block, bcs=[bc], mat_types=[["baij", "aij"], ["aij", ""]])
A1.assemble()
Anorm1 = nest_matrix_norm(A1)
assert Anorm0 == pytest.approx(Anorm1, 1.0e-12)
b1 = assemble_vector_nest(L_block)
apply_lifting_nest(b1, a_block, bcs=[bc])
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L.function_spaces[0] for L in L_block], [bc])
set_bc_nest(b1, bcs0)
b1.assemble()
bnorm1 = math.sqrt(sum([x.norm()**2 for x in b1.getNestSubVecs()]))
assert bnorm0 == pytest.approx(bnorm1, 1.0e-12)
# Monolithic version
E = P0 * P1
W = FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx + inner(u0, v1) * dx + inner(
u1, v0) * dx
L = zero * inner(f, v0) * ufl.dx + inner(g, v1) * dx
a, L = form(a), form(L)
bdofsW_V1 = locate_dofs_topological(W.sub(1), mesh.topology.dim - 1, bndry_facets)
bc = dirichletbc(u_bc, bdofsW_V1, W.sub(1))
A2 = assemble_matrix(a, bcs=[bc])
A2.assemble()
b2 = assemble_vector(L)
apply_lifting(b2, [a], bcs=[[bc]])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, [bc])
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-9)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-9)
# x_extents = mesh_bounding_box(mesh, 0)
# y_extents = mesh_bounding_box(mesh, 1)
# Measures
dx = ufl.Measure("dx", domain=mesh)
ds = ufl.Measure("ds", domain=mesh)
element = ufl.MixedElement([ufl.FiniteElement("Regge", ufl.triangle, r),
ufl.FiniteElement("Lagrange", ufl.triangle, r+1)])
V = FunctionSpace(mesh, element)
V_1 = V.sub(1).collapse()
sigma, u = ufl.TrialFunctions(V)
tau, v = ufl.TestFunctions(V)
def S(tau):
return tau - ufl.Identity(2) * ufl.tr(tau)
# Discrete duality inner product
# cf. eq. 4.5 Lizao Li's PhD thesis
def b(tau_S, v):
n = FacetNormal(mesh)
return inner(tau_S, grad(grad(v))) * dx \
- ufl.dot(ufl.dot(tau_S('+'), n('+')), n('+')) * jump(grad(v), n) * dS \
- ufl.dot(ufl.dot(tau_S, n), n) * ufl.dot(grad(v), n) * ds
sigma_S = S(sigma)
tau_S = S(tau)
f_exact = Constant(mesh, np.array(-1., dtype=PETSc.ScalarType))
def test_assembly_solve_block(mode):
"""Solve a two-field mass-matrix like problem with block matrix approaches
and test that solution is the same.
"""
mesh = UnitSquareMesh(MPI.COMM_WORLD, 32, 31, ghost_mode=mode)
P = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
V0 = dolfinx.fem.FunctionSpace(mesh, P)
V1 = V0.clone()
def boundary(x):
return numpy.logical_or(x[0] < 1.0e-6, x[0] > 1.0 - 1.0e-6)
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = dolfinx.mesh.locate_entities_boundary(
mesh, facetdim, boundary)
bdofsV0 = dolfinx.fem.locate_dofs_topological(V0, facetdim, bndry_facets)
bdofsV1 = dolfinx.fem.locate_dofs_topological(V1, facetdim, bndry_facets)
u_bc0 = dolfinx.fem.Function(V0)
u_bc0.vector.set(50.0)
u_bc0.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
u_bc1 = dolfinx.fem.Function(V1)
u_bc1.vector.set(20.0)
u_bc1.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
bcs = [
dolfinx.fem.dirichletbc.DirichletBC(u_bc0, bdofsV0),
dolfinx.fem.dirichletbc.DirichletBC(u_bc1, bdofsV1)
]
# Variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = dolfinx.Function(V0)
a00 = inner(u, v) * dx
a01 = zero * inner(p, v) * dx
a10 = zero * inner(u, q) * dx
a11 = inner(p, q) * dx
L0 = inner(f, v) * dx
L1 = inner(g, q) * dx
def monitor(ksp, its, rnorm):
pass
# print("Norm:", its, rnorm)
A0 = dolfinx.fem.assemble_matrix_block([[a00, a01], [a10, a11]], bcs)
b0 = dolfinx.fem.assemble_vector_block([L0, L1], [[a00, a01], [a10, a11]],
bcs)
A0.assemble()
A0norm = A0.norm()
b0norm = b0.norm()
x0 = A0.createVecLeft()
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A0)
ksp.setMonitor(monitor)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-14)
ksp.setFromOptions()
ksp.solve(b0, x0)
x0norm = x0.norm()
# Nested (MatNest)
A1 = dolfinx.fem.assemble_matrix_nest([[a00, a01], [a10, a11]],
bcs,
diagonal=1.0)
A1.assemble()
b1 = dolfinx.fem.assemble_vector_nest([L0, L1])
dolfinx.fem.apply_lifting_nest(b1, [[a00, a01], [a10, a11]], bcs)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = dolfinx.cpp.fem.bcs_rows(
dolfinx.fem.assemble._create_cpp_form([L0, L1]), bcs)
dolfinx.fem.set_bc_nest(b1, bcs0)
b1.assemble()
b1norm = b1.norm()
assert b1norm == pytest.approx(b0norm, 1.0e-12)
A1norm = nest_matrix_norm(A1)
assert A0norm == pytest.approx(A1norm, 1.0e-12)
x1 = b1.copy()
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setMonitor(monitor)
ksp.setOperators(A1)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b1, x1)
x1norm = x1.norm()
assert x1norm == pytest.approx(x0norm, rel=1.0e-12)
# Monolithic version
E = P * P
W = dolfinx.fem.FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx
L = inner(f, v0) * ufl.dx + inner(g, v1) * dx
u0_bc = dolfinx.fem.Function(V0)
u0_bc.vector.set(50.0)
u0_bc.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
u1_bc = dolfinx.fem.Function(V1)
u1_bc.vector.set(20.0)
u1_bc.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
bdofsW0_V0 = dolfinx.fem.locate_dofs_topological((W.sub(0), V0), facetdim,
bndry_facets)
bdofsW1_V1 = dolfinx.fem.locate_dofs_topological((W.sub(1), V1), facetdim,
bndry_facets)
bcs = [
dolfinx.fem.dirichletbc.DirichletBC(u0_bc, bdofsW0_V0, W.sub(0)),
dolfinx.fem.dirichletbc.DirichletBC(u1_bc, bdofsW1_V1, W.sub(1))
]
A2 = dolfinx.fem.assemble_matrix(a, bcs)
A2.assemble()
b2 = dolfinx.fem.assemble_vector(L)
dolfinx.fem.apply_lifting(b2, [a], [bcs])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfinx.fem.set_bc(b2, bcs)
A2norm = A2.norm()
b2norm = b2.norm()
assert A2norm == pytest.approx(A0norm, 1.0e-12)
assert b2norm == pytest.approx(b0norm, 1.0e-12)
x2 = b2.copy()
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setMonitor(monitor)
ksp.setOperators(A2)
ksp.setType('cg')
ksp.getPC().setType('jacobi')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b2, x2)
x2norm = x2.norm()
assert x2norm == pytest.approx(x0norm, 1.0e-10)
# Since for this problem the pressure is only determined up to a
# constant, we pin the pressure at the point (0, 0)
zero = Function(Q)
with zero.vector.localForm() as zero_local:
zero_local.set(0.0)
dofs = locate_dofs_geometrical((W.sub(1), Q),
lambda x: np.isclose(x.T, [0, 0, 0]).all(axis=1))
bc2 = DirichletBC(zero, dofs, W.sub(1))
# Collect Dirichlet boundary conditions
bcs = [bc0, bc1, bc2]
# Define variational problem
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
f = Function(W0)
zero = dolfinx.Constant(mesh, 0.0)
a = (inner(grad(u), grad(v)) + inner(p, div(v)) + inner(div(u), q)) * dx
L = inner(f, v) * dx
# Assemble LHS matrix and RHS vector
A = dolfinx.fem.assemble_matrix(a, bcs)
A.assemble()
b = dolfinx.fem.assemble.assemble_vector(L)
dolfinx.fem.assemble.apply_lifting(b, [a], [bcs])
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
# Set Dirichlet boundary condition values in the RHS
dolfinx.fem.assemble.set_bc(b, bcs)
lmbda = 1.0e-02 # surface parameter
dt = 5.0e-06 # time step
theta = 0.5 # time stepping family, e.g. theta=1 -> backward Euler, theta=0.5 -> Crank-Nicolson
# A unit square mesh with 96 cells edges in each direction is created,
# and on this mesh a
# {py:class}`FunctionSpace<dolfinx.fem.FunctionSpace>` `ME` is built
# using a pair of linear Lagrange elements.
msh = create_unit_square(MPI.COMM_WORLD, 96, 96, CellType.triangle)
P1 = ufl.FiniteElement("Lagrange", msh.ufl_cell(), 1)
ME = FunctionSpace(msh, P1 * P1)
# Trial and test functions of the space `ME` are now defined:
q, v = ufl.TestFunctions(ME)
# ```{index} split functions
# ```
#
# For the test functions, {py:func}`TestFunctions<function
# ufl.argument.TestFunctions>` (note the 's' at the end) is used to
# define the scalar test functions `q` and `v`. Some mixed objects
# of the {py:class}`Function<dolfinx.fem.function.Function>` class on
# `ME` are defined to represent $u = (c_{n+1}, \mu_{n+1})$ and
# $u0 = (c_{n}, \mu_{n})$, and these are then split into
# sub-functions:
# +
u = Function(ME) # current solution
u0 = Function(ME) # solution from previous converged step
