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RROMPy/examples/pod/RBTaylor.py
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RBTaylor.py
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import numpy as np
from rrompy.hfengines.linear_problem import \
HelmholtzSquareBubbleProblemEngine as HSBPE
from rrompy.hfengines.linear_problem import \
HelmholtzSquareTransmissionProblemEngine as HSTPE
from rrompy.hfengines.linear_problem import \
HelmholtzBoxScatteringProblemEngine as HBSPE
from rrompy.reduction_methods.taylor import ApproximantTaylorRB as RB
testNo = -1
verb = 100
homog = True
#homog = False
loadName = "RBTaylorModel.pkl"
if testNo in [1, -1]:
if testNo > 0:
params = {'E':4, 'R':4, 'sampleType':'Arnoldi', 'POD':True}
k0 = 12 ** .5
ktar = 10.5 ** .5
solver = HSBPE(kappa = 12 ** .5, theta = np.pi / 3, n = 40,
verbosity = verb)
if testNo > 0:
approx = RB(solver, mu0 = k0, approxParameters = params,
verbosity = verb)
approx.setupApprox()
# approx.plotSamples()
else:
approx = RB(solver, mu0 = 0, verbosity = verb)
approx.loadTrainedModel(loadName)
approx.plotApprox(ktar, name = 'u_RB')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
if testNo > 0:
approx.storeTrainedModel("RBTaylorModel", forceNewFile = False)
print(approx.trainedModel.data.__dict__)
############
elif testNo == 2:
params = {'E':7, 'R':7, 'sampleType':'Arnoldi', 'POD':True}
k0 = 16**.5
ktar = 15**.5
solver = HSTPE(nT = 2, nB = 1, theta = np.pi * 45/180, kappa = 3.,
n = 50, verbosity = verb)
solver.omega = np.real(k0)
approx = RB(solver, mu0 = k0, approxParameters = params,
verbosity = verb, homogeneized = homog)
approx.setupApprox()
# approx.plotSamples()
approx.plotApprox(ktar, name = 'u_RB')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
############
elif testNo in [3, 4]:
if testNo == 3:
params = {'E':8, 'sampleType':'Krylov', 'POD':True}
else:
params = {'E':8, 'sampleType':'Arnoldi', 'POD':True}
k0 = 3
ktar = 4.25+.5j
solver = HBSPE(R = 5, kappa = 3, theta = - np.pi * 75 / 180,
n = 30, verbosity = verb)
solver.omega = np.real(k0)
approx = RB(solver, mu0 = k0, approxParameters = params,
verbosity = verb, homogeneized = homog)
approx.setupApprox()
# approx.plotSamples()
approx.plotApprox(ktar, name = 'u_RB')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
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