sensitivity.itoCorrection(boundary, temperature)
# Time step associated with the iteration.
timeStep = pyslsm.MutableFloat()
# Constraint distance vector.
constraintDistances = pyslsm.VectorDouble()
# Current area mismatch.
mismatch = computeMismatch(levelSet.mesh, targetArea)
# Push current distance from constraint violation into vector.
constraintDistances.append(meshArea * maxMismatch - mismatch)
# Initialise the optimisation object.
optimise = pyslsm.Optimise(boundary.points, constraintDistances, \
lambdas, timeStep, levelSet.moveLimit)
# Perform the optimisation.
optimise.solve()
# Extend boundary point velocities to all narrow band nodes.
levelSet.computeVelocities(boundary.points, timeStep, temperature, rng)
# Compute gradient of the signed distance function within the narrow band.
levelSet.computeGradients()
# Update the level set function.
isReinitialised = levelSet.update(timeStep.value)
# Reinitialise the signed distance function, if necessary.
if not isReinitialised:
print("---------------")
# Integrate until we exceed the maximum time.
while runningTime < maxTime:
# Assign boundary point sensitivities.
for i in range(0, len(boundary.points)):
boundary.points[i].sensitivities[0] = 1.0
# Time step associated with the iteration.
timeStep = pyslsm.MutableFloat()
# Initialise the optimisation object.
# Since there are no constraints we pass an empty vector for the
# constraint distances argument.
optimise = pyslsm.Optimise(boundary.points, pyslsm.VectorDouble(), \
lambdas, timeStep, moveLimit)
# Perform the optimisation.
optimise.solve()
# Extend boundary point velocities to all narrow band nodes.
levelSet.computeVelocities(boundary.points)
# Compute gradient of the signed distance function within the narrow band.
levelSet.computeGradients()
# Update the level set function.
isReinitialised = levelSet.update(timeStep.value)
# Reinitialise the signed distance function, if necessary.
if not isReinitialised: