mp.Volume(center=mp.Vector3(0, 1, 0),
                                         size=mp.Vector3(x=2)),
                               mode=1)
ob_list = [TE0]


def J(alpha):
    return npa.sum(npa.abs(alpha)**2)


#----------------------------------------------------------------------
#- Define optimization problem
#----------------------------------------------------------------------
opt = mpa.OptimizationProblem(simulation=sim,
                              objective_function=J,
                              objective_arguments=ob_list,
                              basis=bases,
                              fcen=fcen,
                              time=time)

#----------------------------------------------------------------------
#- Get gradient
#----------------------------------------------------------------------

f0, g_adjoint = opt()

#----------------------------------------------------------------------
#- FD run
#----------------------------------------------------------------------
db = 1e-3
n = Nx * Ny
choose = 20
Exemple #2
0
#----------------------------------------------------------------------

TE0 = mpa.EigenmodeCoefficient(sim,mp.Volume(center=mp.Vector3(0,1,0),size=mp.Vector3(x=2)),mode=1)
ob_list = [TE0]

def J(alpha):
    return npa.sum(npa.abs(alpha) ** 2)

#----------------------------------------------------------------------
#- Define optimization problem
#----------------------------------------------------------------------
opt = mpa.OptimizationProblem(
    simulation = sim,
    objective_function = J,
    objective_arguments = ob_list,
    basis = [basis],
    fcen = fcen,
    df = fwidth,
    nf = nf,
    time = time
)

#----------------------------------------------------------------------
#- Get gradient
#----------------------------------------------------------------------

f0, g_adjoint = opt()

#----------------------------------------------------------------------
#- FD run
#----------------------------------------------------------------------
db = 1e-3
Exemple #3
0
# objective function and extra objective quantities -------------------
#----------------------------------------------------------------------
splitter = False
#fobj_router   = '|P1_north/P1_west1|^2'
fobj_router = '|P1_east|^2'
fobj_splitter = '( |P1_north| - |P1_east| )^2 + ( |P1_east| - |M1_south| )^2'
objective_function = fobj_router
extra_quantities = ['S_north', 'S_south', 'S_east', 'S_west1', 'S_west2']

#----------------------------------------------------------------------
#----------------------------------------------------------------------
#----------------------------------------------------------------------

opt_prob = mpa.OptimizationProblem(sim,
                                   objective_regions=objective_regions,
                                   objective_function=objective_function,
                                   basis=basis,
                                   beta_vector=beta_vector,
                                   design_function=design_function)
f, _ = opt_prob.get_fdf_funcs()
n = basis.dim
b0 = 9 * np.ones((n, ))
opt_prob.visualize(pmesh=True)
'''plt.savefig('design_region.png')
plt.show()
quit()'''
#----------------------------------------------------------------------
# -- Solve adjoint problem
#----------------------------------------------------------------------
f_adjoint, g_adjoint = opt_prob(b0)

#----------------------------------------------------------------------