def updateStatistics(self):
for curLevel in range(len(self.space4Level)):
samples = self.y4Level[curLevel]
self.variance4level[curLevel] = np.var(samples, 0)
compCost = self.space4Level[curLevel].dim() ** 2 / 1000.
# Simple for uniform meshes
# optSamples = self.constCompCost * np.sqrt(
# np.sqrt(np.sqrt(np.mean(np.abs(self.variance4level[curLevel]))) / compCost))
# Same but for general meshes
space = self.space4Level[curLevel]
domainArea = assemble(interpolate(Constant(1), space) * dx)
varianceMean = Function(space)
varianceMean.vector().set_local(np.abs(self.variance4level[curLevel]))
varianceIntMean = np.sqrt(assemble(varianceMean * dx) / domainArea)
optSamples = self.constCompCost * np.sqrt(np.sqrt(
varianceIntMean / compCost))
# Alernate from ???
# l = curLevel + 2
# maxL = len(self.space4Level) + 1
# epsOptSamples = 0.1
# optSamples = l ** (2 + 2 * epsOptSamples) * 2 ** (2 * (maxL - l))
self.optNumSamples4Level[curLevel] = np.rint(optSamples).astype(int)
self.compCost4lvl[-1] = sum(list(len(samples) * (space.dim() ** 2)
for (space, samples) in zip(self.space4Level, self.y4Level)))
def local_mass_matrix():
# Define mesh
mesh = UnitIntervalMesh(1)
# Define function space
V = FunctionSpace(mesh, "Lagrange", 1)
# Define trial and test functions
u = TrialFunction(V)
v = TestFunction(V)
# Define bilinear form
a = (dot(grad(u), grad(v)) + u * v) * dx
# Assemble matrix
A = assemble(a)
print(A.array())
# Bilinear form for just the mass matrix part
a2 = u * v * dx
# Assemble mass matrix
A2 = assemble(a2)
print(6 * A2.array())
def _assemble_system_control_diag(a, dummy, bcs, diag_value):
A = PETScMatrix()
assemble(a, tensor=A)
dummy_vec = assemble(dummy)
for bc in bcs:
bc.zero(A)
bc.zero_columns(A, dummy_vec, diag_value)
return A
def tv_reg(lmda, mu, dx, R_lmda=1, R_mu=1, eps=1e-10):
grad_lamda = grad(lmda)
grad_mu = grad(mu)
integrand_lmda = R_lmda * sqrt(inner(grad_lamda, grad_lamda) + eps) * dx
integrand_mu = R_mu * sqrt(inner(grad_mu, grad_mu) + eps) * dx
return assemble(integrand_lmda + integrand_mu)
def tn_reg(lmda, mu, dx, R_lmda=1, R_mu=1):
grad_lamda = grad(lmda)
grad_mu = grad(mu)
reg = 0.5 * R_lmda * inner(grad_lamda, grad_lamda) * dx + \
0.5 * R_mu * inner(grad_mu, grad_mu) * dx
Reg = assemble(reg)
return Reg
def estimate(self, solution):
mesh = solution.function_space().mesh()
# Define cell and facet residuals
R_T = -(self.rhs_f + div(grad(solution)))
n = FacetNormal(mesh)
R_dT = dot(grad(solution), n)
# Will use space of constants to localize indicator form
Constants = FunctionSpace(mesh, "DG", 0)
w = TestFunction(Constants)
h = CellSize(mesh)
# Define form for assembling error indicators
form = (h ** 2 * R_T ** 2 * w * dx + avg(h) * avg(R_dT) ** 2 * 2 * avg(w) * dS)
# + h * R_dT ** 2 * w * ds)
# Assemble error indicators
indicators = assemble(form)
# Calculate error
error_estimate = sqrt(sum(i for i in indicators.array()))
# Take sqrt of indicators
indicators = np.array([sqrt(i) for i in indicators])
# Mark cells for refinement based on maximal marking strategy
largest_error = max(indicators)
cell_markers = MeshFunction("bool", mesh, mesh.topology().dim())
for c in cells(mesh):
cell_markers[c] = indicators[c.index()] > (self.fraction * largest_error)
return error_estimate, cell_markers
def computeError4level(self, meanApprox4lvl, lastSpace, meanExact = None):
if meanExact == None:
solMesh = lastSpace.mesh()
solFamily = lastSpace.ufl_element().family()
solDegree = lastSpace.ufl_element().degree()
refSpace = FunctionSpace(refine(refine(solMesh)), solFamily, solDegree)
meanExact = self.solveMean(refSpace)
refSpace = meanExact.function_space()
error4lvl = list(
np.sqrt(assemble(
(project(meanApprox, refSpace) - interpolate(meanExact, refSpace)) ** 2 * dx
)) for meanApprox in meanApprox4lvl)
return error4lvl
def compute_error(u1, u2):
# Reference mesh
mesh_resolution_ref = 500
mesh_ref = UnitIntervalMesh(mesh_resolution_ref)
# Reference function space
V_ref = FunctionSpace(mesh_ref, "CG", 1)
# Evaluate the input functions on the reference mesh
Iu1 = interpolate(u1, V_ref)
Iu2 = interpolate(u2, V_ref)
# Compute the error
e = Iu1 - Iu2
error = sqrt(assemble(e * e * dx))
return error
def plot_basis_function():
# Declare mesh and FEM functions
mesh = UnitSquareMesh(10, 10)
V = FunctionSpace(mesh, "CG", 1)
phi = Function(V)
phi.vector()[:] = 0.0
phi.vector()[10] = 1.0
# Plot the basis function
fig = plt.figure()
plot(phi, fig=fig)
plt.show()
val = assemble(phi * phi * dx)
print(val)
def global_stiffness_matrix():
# Define mesh
mesh = UnitIntervalMesh(10)
# Define function space
V = FunctionSpace(mesh, "Lagrange", 1)
# Define trial and test functions
u = TrialFunction(V)
v = TestFunction(V)
# Define bilinear form
a = dot(grad(u), grad(v)) * dx
# Assemble matrix
A = assemble(a)
h = 1 / 10
print(h * A.array())
# Defining displacement space V = VectorFunctionSpace(mesh, finiteElement.family, finiteElement.order) # Stresss space S = TensorFunctionSpace(mesh, finiteElement.family, finiteElement.order) # Trial and test functions uTrial = TrialFunction(V) w = TestFunction(V) # weak operators massOp = physics.rho*inner(uTrial, w)*dx stiffOp = inner(elasticity.stress(physics.mu, physics.lmbda, uTrial), grad(w))*dx # Assembling M = assemble(massOp) R = assemble(stiffOp) ## Create zero boundary condition #bc = DirichletBC(V, Constant(geo.nDim*[0.0]), dirichletSubdomain) ## Construct dirichlet contribution vectors #dirichletM = PETScVector(MPI_COMM_WORLD, M.size(0)) #bc.zero(M) #bc.zero_columns(M, dirichletM, 1) #dirichletR = PETScVector(MPI_COMM_WORLD, R.size(0)) #bc.zero(R) #bc.zero_columns(R, dirichletR, 1) ### ### Output configuration
def solve_navier_stokes_equation(interior_circle=True, num_mesh_refinements=0):
"""
Solve the Navier-Stokes equation on a hard-coded mesh with hard-coded initial and boundary conditions
"""
mesh, om, im, nm, ymax, sub_domains = setup_geometry(
interior_circle, num_mesh_refinements)
dsi = Measure("ds", domain=mesh, subdomain_data=sub_domains)
# Setup FEM function spaces
# Function space for the velocity
V = VectorFunctionSpace(mesh, "CG", 1)
# Function space for the pressure
Q = FunctionSpace(mesh, "CG", 1)
# Mixed function space for velocity and pressure
W = V * Q
# Setup FEM functions
v, q = TestFunctions(W)
w = Function(W)
(u, p) = (as_vector((w[0], w[1])), w[2])
u0 = Function(V)
# Inlet velocity
uin = Expression(("4*(x[1]*(YMAX-x[1]))/(YMAX*YMAX)", "0."),
YMAX=ymax,
degree=1)
# Viscosity and stabilization parameters
nu = 1e-6
h = CellSize(mesh)
d = 0.2 * h**(3.0 / 2.0)
# Time parameters
time_step = 0.1
t_start, t_end = 0.0, 10.0
# Penalty parameter
gamma = 10 / h
# Time stepping
t = t_start
step = 0
while t < t_end:
# Time discretization (CrankâNicolson method)
um = 0.5 * u + 0.5 * u0
# Navier-Stokes equations in weak residual form (stabilized FEM)
# Basic residual
r = (inner((u - u0) / time_step + grad(p) + grad(um) * um, v) +
nu * inner(grad(um), grad(v)) + div(um) * q) * dx
# Weak boundary conditions
r += gamma * (om * p * q + im * inner(u - uin, v) +
nm * inner(u, v)) * ds
# Stabilization
r += d * (inner(grad(p) + grad(um) * um,
grad(q) + grad(um) * v) + inner(div(um), div(v))) * dx
# Solve the Navier-Stokes equation (one time step)
solve(r == 0, w)
if step % 5 == 0:
# Plot norm of velocity at current time step
nov = project(sqrt(inner(u, u)), Q)
fig = plt.figure()
plot(nov, fig=fig)
plt.show()
# Compute drag force on circle
n = FacetNormal(mesh)
drag_force_measure = p * n[0] * dsi(1) # Drag (only pressure)
drag_force = assemble(drag_force_measure)
print("Drag force = " + str(drag_force))
# Shift to next time step
t += time_step
step += 1
u0 = project(u, V)
def L2_norm(u, dx):
energy = inner(u, u) * dx
E = assemble(energy)
return p.sqrt(E)
# Save solution in XDMF format (to be viewed in Paraview, for example)
with XDMFFile(MPI.comm_world,
"plane_wave.xdmf",
encoding=XDMFFile.Encoding.HDF5) as file:
file.write(u)
''' Calculate L2 and H1 errors of FEM solution and best approximation.
This demonstrates the error bounds given in Ihlenburg.
Pollution errors are evident for high wavenumbers.'''
# Function space for exact solution - need it to be higher than deg
V_exact = FunctionSpace(mesh, "Lagrange", deg + 3)
# "exact" solution
u_exact = interpolate(ui, V_exact)
# best approximation from V
u_BA = interpolate(ui, V)
# H1 errors
diff = u - u_exact
diff_BA = u_BA - u_exact
H1_diff = np.sqrt(assemble(inner(grad(diff), grad(diff)) * dx))
H1_BA = np.sqrt(assemble(inner(grad(diff_BA), grad(diff_BA)) * dx))
H1_exact = np.sqrt(assemble(inner(grad(u_exact), grad(u_exact)) * dx))
print('Relative H1 error of best approximation:', H1_BA / H1_exact)
print('Relative H1 error of FEM solution:', H1_diff / H1_exact)
# L2 errors
L2_diff = np.sqrt(assemble(inner(diff, diff) * dx))
L2_BA = np.sqrt(assemble(inner(diff_BA, diff_BA) * dx))
L2_exact = np.sqrt(assemble(inner(u_exact, u_exact) * dx))
print('Relative L2 error of best approximation:', L2_BA / L2_exact)
print('Relative L2 error of FEM solution:', L2_diff / L2_exact)
grad(w)) * dx
# basisOp = [ inner(psi, w)*ds(i) for psi in psiBasis for i in range(geo.getNoFaces()) ]
basisOp = [inner(psi, w) * ds(1) for psi in psiBasis]
## Matrices and vectors
#M = PETScMatrix()
#R = PETScMatrix()
#P = [ PETScVector() for i in range(len(basisOp)) ]
## Assembling
#assemble(massOp, tensor=M)
#assemble(stiffOp, tensor=R)
#[ assemble(basisOp[i], tensor=P[i]) for i in range(len(P)) ]
# Assembling
M = assemble(massOp)
R = assemble(stiffOp)
FPsi = [assemble(op) for op in basisOp]
nPsi = len(FPsi)
###
### Output configuration
###
outputFolder = "./new_results/"
pvdFileU = [
File(outputFolder + "/" + str(i) + "/u_psi.pvd", "compressed")
for i in range(nPsi)
]
pvdFileV = [
def norm(v, norm_type="L2", mesh=None):
"""
Return the norm of a given vector or function.
*Arguments*
v
a :py:class:`Vector <dolfin.cpp.Vector>` or
a :py:class:`Function <dolfin.functions.function.Function>`.
norm_type
see below for alternatives.
mesh
optional :py:class:`Mesh <dolfin.cpp.Mesh>` on
which to compute the norm.
If the norm type is not specified, the standard :math:`L^2` -norm
is computed. Possible norm types include:
*Vectors*
================ ================= ================
Norm Usage
================ ================= ================
:math:`l^2` norm(x, 'l2') Default
:math:`l^1` norm(x, 'l1')
:math:`l^\infty` norm(x, 'linf')
================ ================= ================
*Functions*
================ ================= =================================
Norm Usage Includes the :math:`L^2` -term
================ ================= =================================
:math:`L^2` norm(v, 'L2') Yes
:math:`H^1` norm(v, 'H1') Yes
:math:`H^1_0` norm(v, 'H10') No
:math:`H` (div) norm(v, 'Hdiv') Yes
:math:`H` (div) norm(v, 'Hdiv0') No
:math:`H` (curl) norm(v, 'Hcurl') Yes
:math:`H` (curl) norm(v, 'Hcurl0') No
================ ================= =================================
*Examples of usage*
.. code-block:: python
v = Function(V)
x = v.vector()
print norm(x, 'linf') # print the infinity norm of vector x
n = norm(v) # compute L^2 norm of v
print norm(v, 'Hdiv') # print H(div) norm of v
n = norm(v, 'H1', mesh) # compute H^1 norm of v on given mesh
"""
# if not isinstance(v, (GenericVector, GenericFunction)):
# cpp.dolfin_error("norms.py",
# "compute norm",
# "expected a GenericVector or GenericFunction")
# Check arguments
# if not isinstance(norm_type, string_types):
# cpp.dolfin_error("norms.py",
# "compute norm",
# "Norm type must be a string, not " +
# str(type(norm_type)))
# if mesh is not None and not isinstance(mesh, cpp.Mesh):
# cpp.dolfin_error("norms.py",
# "compute norm",
# "Expecting a Mesh, not " + str(type(mesh)))
# Get mesh from function
if isinstance(v, cpp.function.Function) and mesh is None:
mesh = v.function_space().mesh()
# Define integration measure and domain
dx = ufl.dx(mesh)
# Select norm type
if isinstance(v, cpp.la.GenericVector):
return v.norm(norm_type.lower())
elif isinstance(v, ufl.Coefficient):
if norm_type.lower() == "l2":
M = v**2*dx
elif norm_type.lower() == "h1":
M = (v**2 + grad(v)**2)*dx
elif norm_type.lower() == "h10":
M = grad(v)**2*dx
elif norm_type.lower() == "hdiv":
M = (v**2 + div(v)**2)*dx
elif norm_type.lower() == "hdiv0":
M = div(v)**2*dx
elif norm_type.lower() == "hcurl":
M = (v**2 + curl(v)**2)*dx
elif norm_type.lower() == "hcurl0":
M = curl(v)**2*dx
# else:
# cpp.dolfin_error("norms.py",
# "compute norm",
# "Unknown norm type (\"%s\") for functions"
# % str(norm_type))
# else:
# cpp.dolfin_error("norms.py",
# "compute norm",
# "Unknown object type. Must be a vector or a function")
# Assemble value
r = assemble(M)
# Check value
if r < 0.0:
pass
# cpp.dolfin_error("norms.py",
# "compute norm",
# "Square of norm is negative, might be a round-off error")
elif r == 0.0:
return 0.0
else:
return sqrt(r)
def norm(v, norm_type="L2", mesh=None):
"""
Return the norm of a given vector or function.
*Arguments*
v
a :py:class:`Vector <dolfin.cpp.Vector>` or
a :py:class:`Function <dolfin.functions.function.Function>`.
norm_type
see below for alternatives.
mesh
optional :py:class:`Mesh <dolfin.cpp.Mesh>` on
which to compute the norm.
If the norm type is not specified, the standard :math:`L^2` -norm
is computed. Possible norm types include:
*Vectors*
================ ================= ================
Norm Usage
================ ================= ================
:math:`l^2` norm(x, 'l2') Default
:math:`l^1` norm(x, 'l1')
:math:`l^\infty` norm(x, 'linf')
================ ================= ================
*Functions*
================ ================= =================================
Norm Usage Includes the :math:`L^2` -term
================ ================= =================================
:math:`L^2` norm(v, 'L2') Yes
:math:`H^1` norm(v, 'H1') Yes
:math:`H^1_0` norm(v, 'H10') No
:math:`H` (div) norm(v, 'Hdiv') Yes
:math:`H` (div) norm(v, 'Hdiv0') No
:math:`H` (curl) norm(v, 'Hcurl') Yes
:math:`H` (curl) norm(v, 'Hcurl0') No
================ ================= =================================
*Examples of usage*
.. code-block:: python
v = Function(V)
x = v.vector()
print norm(x, 'linf') # print the infinity norm of vector x
n = norm(v) # compute L^2 norm of v
print norm(v, 'Hdiv') # print H(div) norm of v
n = norm(v, 'H1', mesh) # compute H^1 norm of v on given mesh
"""
if not isinstance(v, (GenericVector, GenericFunction)):
raise TypeError, "expected a GenericVector or GenericFunction"
# Check arguments
if not isinstance(norm_type, str):
cpp.dolfin_error(
"norms.py", "compute norm",
"Norm type must be a string, not " + str(type(norm_type)))
if mesh is not None and not isinstance(mesh, Mesh):
cpp.dolfin_error("norms.py", "compute norm",
"Expecting a Mesh, not " + str(type(mesh)))
# Select norm type
if isinstance(v, GenericVector):
return v.norm(norm_type.lower())
elif (isinstance(v, Coefficient) and isinstance(v, GenericFunction)):
if norm_type.lower() == "l2":
M = inner(v, v) * dx()
elif norm_type.lower() == "h1":
M = inner(v, v) * dx() + inner(grad(v), grad(v)) * dx()
elif norm_type.lower() == "h10":
M = inner(grad(v), grad(v)) * dx()
elif norm_type.lower() == "hdiv":
M = inner(v, v) * dx() + div(v) * div(v) * dx()
elif norm_type.lower() == "hdiv0":
M = div(v) * div(v) * dx()
elif norm_type.lower() == "hcurl":
M = inner(v, v) * dx() + inner(curl(v), curl(v)) * dx()
elif norm_type.lower() == "hcurl0":
M = inner(curl(v), curl(v)) * dx()
else:
cpp.dolfin_error(
"norms.py", "compute norm",
"Unknown norm type (\"%s\") for functions" % str(norm_type))
else:
cpp.dolfin_error(
"norms.py", "compute norm",
"Unknown object type. Must be a vector or a function")
# Get mesh
if isinstance(v, Function) and mesh is None:
mesh = v.function_space().mesh()
# Assemble value
r = assemble(M,
mesh=mesh,
form_compiler_parameters={"representation": "quadrature"})
# Check value
if r < 0.0:
cpp.dolfin_error(
"norms.py", "compute norm",
"Square of norm is negative, might be a round-off error")
elif r == 0.0:
return 0.0
else:
return sqrt(r)
def norm(v, norm_type="L2", mesh=None):
"""
Return the norm of a given vector or function.
*Arguments*
v
a :py:class:`Vector <dolfin.cpp.Vector>` or
a :py:class:`Function <dolfin.functions.function.Function>`.
norm_type
see below for alternatives.
mesh
optional :py:class:`Mesh <dolfin.cpp.Mesh>` on
which to compute the norm.
If the norm type is not specified, the standard :math:`L^2` -norm
is computed. Possible norm types include:
*Vectors*
================ ================= ================
Norm Usage
================ ================= ================
:math:`l^2` norm(x, 'l2') Default
:math:`l^1` norm(x, 'l1')
:math:`l^\infty` norm(x, 'linf')
================ ================= ================
*Functions*
================ ================= =================================
Norm Usage Includes the :math:`L^2` -term
================ ================= =================================
:math:`L^2` norm(v, 'L2') Yes
:math:`H^1` norm(v, 'H1') Yes
:math:`H^1_0` norm(v, 'H10') No
:math:`H` (div) norm(v, 'Hdiv') Yes
:math:`H` (div) norm(v, 'Hdiv0') No
:math:`H` (curl) norm(v, 'Hcurl') Yes
:math:`H` (curl) norm(v, 'Hcurl0') No
================ ================= =================================
*Examples of usage*
.. code-block:: python
v = Function(V)
x = v.vector()
print norm(x, 'linf') # print the infinity norm of vector x
n = norm(v) # compute L^2 norm of v
print norm(v, 'Hdiv') # print H(div) norm of v
n = norm(v, 'H1', mesh) # compute H^1 norm of v on given mesh
"""
if not isinstance(v, (GenericVector, GenericFunction)):
raise TypeError, "expected a GenericVector or GenericFunction"
# Check arguments
if not isinstance(norm_type, str):
cpp.dolfin_error("norms.py",
"compute norm",
"Norm type must be a string, not " + str(type(norm_type)))
if mesh is not None and not isinstance(mesh, Mesh):
cpp.dolfin_error("norms.py",
"compute norm",
"Expecting a Mesh, not " + str(type(mesh)))
# Select norm type
if isinstance(v, GenericVector):
return v.norm(norm_type.lower())
elif (isinstance(v, Coefficient) and isinstance(v, GenericFunction)):
if norm_type.lower() == "l2":
M = inner(v, v)*dx()
elif norm_type.lower() == "h1":
M = inner(v, v)*dx() + inner(grad(v), grad(v))*dx()
elif norm_type.lower() == "h10":
M = inner(grad(v), grad(v))*dx()
elif norm_type.lower() == "hdiv":
M = inner(v, v)*dx() + div(v)*div(v)*dx()
elif norm_type.lower() == "hdiv0":
M = div(v)*div(v)*dx()
elif norm_type.lower() == "hcurl":
M = inner(v, v)*dx() + inner(curl(v), curl(v))*dx()
elif norm_type.lower() == "hcurl0":
M = inner(curl(v), curl(v))*dx()
else:
cpp.dolfin_error("norms.py",
"compute norm",
"Unknown norm type (\"%s\") for functions" % str(norm_type))
else:
cpp.dolfin_error("norms.py",
"compute norm",
"Unknown object type. Must be a vector or a function")
# Get mesh
if isinstance(v, Function) and mesh is None:
mesh = v.function_space().mesh()
# Assemble value
r = assemble(M, mesh=mesh, form_compiler_parameters={"representation": "quadrature"})
# Check value
if r < 0.0:
cpp.dolfin_error("norms.py",
"compute norm",
"Square of norm is negative, might be a round-off error")
elif r == 0.0:
return 0.0
else:
return sqrt(r)
# Define variational problem
F = rho*inner(a_*N_ddot(u) + b_*N_dot(u) + c_*u, w)*dx \
+ inner(N_dot(S).T*Lambda_e + S.T*Lambda_p, grad(w))*dx \
- inner(g, w)*ds \
+ inner(compliance(a_*N_ddot(S) + b_*N_dot(S) + c_*S), T)*dx \
- 0.5*inner(grad(u)*Lambda_p + Lambda_p*grad(u).T + grad(N_dot(u))*Lambda_e \
+ Lambda_e*grad(N_dot(u)).T, T)*dx \
#- inner(f, w)*dx(1) \
a, L = lhs(F), rhs(F)
# Assemble rhs (once)
A = assemble(a)
# Create GMRES Krylov solver
solver = KrylovSolver(A, "gmres")
# Create solution function
S = Function(W)
# Solving loop
xdmf_file = XDMFFile(mesh.mpi_comm(), "output/elastodynamics.xdmf")
if record:
pvd = File("paraview/{}.pvd".format(file_name))
pvd << (u0, t)
]
g = sum(pulses)
F = rho * inner(a_ * N_ddot(u, u0, a0, v0, dt, beta) \
+ b_ * N_dot(u, u0, v0, a0, dt, beta, gamma) + c_ * u, w) * dx \
+ inner(N_dot(S, U0, V0, A0, dt, beta, gamma).T * Lambda_e + S.T * Lambda_p, grad(w)) * dx \
- inner(g, w) * ds \
+ inner(compliance(a_ * N_ddot(S, U0, A0, V0, dt, beta) + b_ * N_dot(S, U0, V0, A0, dt, beta, gamma) + c_ * S, u, mu, lmbda), T) * dx \
- 0.5 * inner(grad(u) * Lambda_p + Lambda_p * grad(u).T + grad(N_dot(u, u0, v0, a0, dt, beta, gamma)) * Lambda_e \
+ Lambda_e * grad(N_dot(u, u0, v0, a0, dt, beta, gamma)).T, T) * dx \
a, L = lhs(F), rhs(F)
# Assemble rhs (once)
A = assemble(a)
# Create GMRES Krylov solver
solver = KrylovSolver(A, "gmres")
# Create solution function
S = Function(W)
experiment_count_file = open("experiment_counter", 'rb')
experiment_count = pickle.load(experiment_count_file)
experiment_count_file.close()
paraview_file_name = "experiment_{}".format(experiment_count)
info_file_name = "{}_experiments_info/info_n{}.txt".format(
type_of_medium, experiment_count)
def forward(mu_expression,
lmbda_expression,
rho,
Lx=10,
Ly=10,
t_end=1,
omega_p=5,
amplitude=5000,
center=0,
target=False):
Lpml = Lx / 10
#c_p = cp(mu.vector(), lmbda.vector(), rho)
max_velocity = 200 #c_p.max()
stable_hx = stable_dx(max_velocity, omega_p)
nx = int(Lx / stable_hx) + 1
#nx = max(nx, 60)
ny = int(Ly * nx / Lx) + 1
mesh = mesh_generator(Lx, Ly, Lpml, nx, ny)
used_hx = Lx / nx
dt = stable_dt(used_hx, max_velocity)
cfl_ct = cfl_constant(max_velocity, dt, used_hx)
print(used_hx, stable_hx)
print(cfl_ct)
#time.sleep(10)
PE = FunctionSpace(mesh, "DG", 0)
mu = interpolate(mu_expression, PE)
lmbda = interpolate(lmbda_expression, PE)
m = 2
R = 10e-8
t = 0.0
gamma = 0.50
beta = 0.25
ff = MeshFunction("size_t", mesh, mesh.geometry().dim() - 1)
Dirichlet(Lx, Ly, Lpml).mark(ff, 1)
# Create function spaces
VE = VectorElement("CG", mesh.ufl_cell(), 1, dim=2)
TE = TensorElement("DG", mesh.ufl_cell(), 0, shape=(2, 2), symmetry=True)
W = FunctionSpace(mesh, MixedElement([VE, TE]))
F = FunctionSpace(mesh, "CG", 2)
V = W.sub(0).collapse()
M = W.sub(1).collapse()
alpha_0 = Alpha_0(m, stable_hx, R, Lpml)
alpha_1 = Alpha_1(alpha_0, Lx, Lpml, degree=2)
alpha_2 = Alpha_2(alpha_0, Ly, Lpml, degree=2)
beta_0 = Beta_0(m, max_velocity, R, Lpml)
beta_1 = Beta_1(beta_0, Lx, Lpml, degree=2)
beta_2 = Beta_2(beta_0, Ly, Lpml, degree=2)
alpha_1 = interpolate(alpha_1, F)
alpha_2 = interpolate(alpha_2, F)
beta_1 = interpolate(beta_1, F)
beta_2 = interpolate(beta_2, F)
a_ = alpha_1 * alpha_2
b_ = alpha_1 * beta_2 + alpha_2 * beta_1
c_ = beta_1 * beta_2
Lambda_e = as_tensor([[alpha_2, 0], [0, alpha_1]])
Lambda_p = as_tensor([[beta_2, 0], [0, beta_1]])
a_ = alpha_1 * alpha_2
b_ = alpha_1 * beta_2 + alpha_2 * beta_1
c_ = beta_1 * beta_2
Lambda_e = as_tensor([[alpha_2, 0], [0, alpha_1]])
Lambda_p = as_tensor([[beta_2, 0], [0, beta_1]])
# Set up boundary condition
bc = DirichletBC(W.sub(0), Constant(("0.0", "0.0")), ff, 1)
# Create measure for the source term
dx = Measure("dx", domain=mesh)
ds = Measure("ds", domain=mesh, subdomain_data=ff)
# Set up initial values
u0 = Function(V)
u0.set_allow_extrapolation(True)
v0 = Function(V)
a0 = Function(V)
U0 = Function(M)
V0 = Function(M)
A0 = Function(M)
# Test and trial functions
(u, S) = TrialFunctions(W)
(w, T) = TestFunctions(W)
g = ModifiedRickerPulse(0, omega_p, amplitude, center)
F = rho * inner(a_ * N_ddot(u, u0, a0, v0, dt, beta) \
+ b_ * N_dot(u, u0, v0, a0, dt, beta, gamma) + c_ * u, w) * dx \
+ inner(N_dot(S, U0, V0, A0, dt, beta, gamma).T * Lambda_e + S.T * Lambda_p, grad(w)) * dx \
- inner(g, w) * ds \
+ inner(compliance(a_ * N_ddot(S, U0, A0, V0, dt, beta) + b_ * N_dot(S, U0, V0, A0, dt, beta, gamma) + c_ * S, u, mu, lmbda), T) * dx \
- 0.5 * inner(grad(u) * Lambda_p + Lambda_p * grad(u).T + grad(N_dot(u, u0, v0, a0, dt, beta, gamma)) * Lambda_e \
+ Lambda_e * grad(N_dot(u, u0, v0, a0, dt, beta, gamma)).T, T) * dx \
a, L = lhs(F), rhs(F)
# Assemble rhs (once)
A = assemble(a)
# Create GMRES Krylov solver
solver = KrylovSolver(A, "gmres")
# Create solution function
S = Function(W)
if target:
xdmffile_u = XDMFFile("inversion_temporal_file/target/u.xdmf")
pvd = File("inversion_temporal_file/target/u.pvd")
xdmffile_u.write(u0, t)
timeseries_u = TimeSeries(
"inversion_temporal_file/target/u_timeseries")
else:
xdmffile_u = XDMFFile("inversion_temporal_file/obs/u.xdmf")
xdmffile_u.write(u0, t)
timeseries_u = TimeSeries("inversion_temporal_file/obs/u_timeseries")
rec_counter = 0
while t < t_end - 0.5 * dt:
t += float(dt)
if rec_counter % 10 == 0:
print(
'\n\rtime: {:.3f} (Progress: {:.2f}%)'.format(
t, 100 * t / t_end), )
g.t = t
# Assemble rhs and apply boundary condition
b = assemble(L)
bc.apply(A, b)
# Compute solution
solver.solve(S.vector(), b)
(u, U) = S.split(True)
# Update previous time step
update(u, u0, v0, a0, beta, gamma, dt)
update(U, U0, V0, A0, beta, gamma, dt)
xdmffile_u.write(u, t)
pvd << (u, t)
timeseries_u.store(u.vector(), t)
energy = inner(u, u) * dx
E = assemble(energy)
print("E = ", E)
print(u.vector().max())