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greedy_internalBox.py
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Sun, Apr 28, 06:28

greedy_internalBox.py
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import numpy as np
import fenics as fen
from rrompy.hfengines.linear_problem import HelmholtzProblemEngine as HPE
from rrompy.reduction_methods.greedy import RationalInterpolantGreedy as RI
from rrompy.reduction_methods.greedy import ReducedBasisGreedy as RB
from rrompy.solver.fenics import L2NormMatrix
from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
dim = 2
verb = 5
timed = False
algo = "RI"
#algo = "RB"
polyBasis = "LEGENDRE"
polyBasis = "CHEBYSHEV"
#polyBasis = "MONOMIAL"
k0s = np.power(np.linspace(500 ** 2., 2250 ** 2., 200), .5)
k0 = np.mean(np.power(k0s, 2.)) ** .5
kl, kr = min(k0s), max(k0s)
params = {'sampler':QS([kl, kr], "UNIFORM", 2.), 'nTestPoints':500,
'greedyTol':1e-2, 'S':2, 'polybasis':polyBasis,
# 'errorEstimatorKind':'AFFINE'}
'errorEstimatorKind':'DISCREPANCY'}
# 'errorEstimatorKind':'INTERPOLATORY'}
# 'errorEstimatorKind':'LOCALL2'}
if dim == 2:
mesh = fen.RectangleMesh(fen.Point(0., 0.), fen.Point(.1, .15), 10, 15)
x, y = fen.SpatialCoordinate(mesh)[:]
f = fen.exp(- 1e2 * (x + y))
else:#if dim == 3:
mesh = fen.BoxMesh(fen.Point(0., 0., 0.), fen.Point(.1, .15, .25), 4, 6,10)
x, y, z = fen.SpatialCoordinate(mesh)[:]
f = fen.exp(- 1e2 * (x + y + z))
solver = HPE(np.real(k0), verbosity = verb)
solver.V = fen.FunctionSpace(mesh, "P", 3)
solver.refractionIndex = fen.Constant(1. / 54.6)
solver.forcingTerm = f
solver.NeumannBoundary = "ALL"
#########
if algo == "RI":
approx = RI(solver, mu0 = k0, approxParameters = params, verbosity = verb)
else:
params.pop("polybasis")
params.pop("errorEstimatorKind")
approx = RB(solver, mu0 = k0, approxParameters = params, verbosity = verb)
approx.initEstimatorNormEngine(L2NormMatrix(solver.V))
if timed:
from time import clock
start_time = clock()
approx.greedy()
print("Time: ", clock() - start_time)
else:
approx.greedy(True)
approx.samplingEngine.verbosity = 0
approx.verbosity = 0
kl, kr = np.real(kl), np.real(kr)
from matplotlib import pyplot as plt
normApp = np.zeros(len(k0s))
norm = np.zeros_like(normApp)
res = np.zeros_like(normApp)
err = np.zeros_like(normApp)
for j in range(len(k0s)):
normApp[j] = approx.normApprox(k0s[j])
norm[j] = approx.normHF(k0s[j])
res[j] = (approx.estimatorNormEngine.norm(
approx.getRes(k0s[j], duality = False))
/ approx.estimatorNormEngine.norm(
approx.getRHS(k0s[j], duality = False)))
err[j] = approx.normErr(k0s[j]) / approx.normHF(k0s[j])
resApp = approx.errorEstimator(k0s)
plt.figure()
plt.plot(k0s, norm)
plt.plot(k0s, normApp, '--')
plt.plot(np.real(approx.mus(0)),
1.05*np.max(norm)*np.ones(approx.mus.shape, dtype = float), 'rx')
plt.xlim([kl, kr])
plt.grid()
plt.show()
plt.close()
plt.figure()
plt.semilogy(k0s, res)
plt.semilogy(k0s, resApp, '--')
plt.semilogy(np.real(approx.mus(0)),
4.*np.max(resApp)*np.ones(approx.mus.shape, dtype = float), 'rx')
plt.xlim([kl, kr])
plt.grid()
plt.show()
plt.close()
plt.figure()
plt.semilogy(k0s, err)
plt.xlim([kl, kr])
plt.grid()
plt.show()
plt.close()
polesApp = approx.getPoles()
mask = (np.real(polesApp) < kl) | (np.real(polesApp) > kr)
print("Outliers:", polesApp[mask])
polesApp = polesApp[~mask]
plt.figure()
plt.plot(np.real(polesApp), np.imag(polesApp), 'kx')
plt.axis('equal')
plt.grid()
plt.show()
plt.close()

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