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rb_centered.py
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Sat, May 4, 18:08
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R6746 RationalROMPy
rb_centered.py
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# Copyright (C) 2018 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
from
copy
import
deepcopy
as
copy
import
numpy
as
np
from
.generic_centered_approximant
import
GenericCenteredApproximant
from
rrompy.reduction_methods.trained_model
import
TrainedModelRB
as
tModel
from
rrompy.reduction_methods.trained_model
import
TrainedModelData
from
rrompy.reduction_methods.base.rb_utils
import
projectAffineDecomposition
from
rrompy.utilities.base.types
import
(
Np1D
,
Np2D
,
Tuple
,
List
,
DictAny
,
HFEng
,
paramVal
,
sampList
)
from
rrompy.utilities.base
import
purgeDict
,
verbosityDepth
from
rrompy.utilities.exception_manager
import
RROMPyException
,
RROMPyWarning
__all__
=
[
'RBCentered'
]
class
RBCentered
(
GenericCenteredApproximant
):
"""
ROM single-point fast RB approximant computation for parametric problems
with polynomial dependence up to degree 2.
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': whether to compute POD of snapshots; defaults to True;
- 'R': rank for Galerkin projection; defaults to E + 1;
- 'E': total number of derivatives current approximant relies upon;
defaults to 1.
Defaults to empty dict.
homogeneized(optional): Whether to homogeneize Dirichlet BCs. Defaults
to False.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
homogeneized: Whether to homogeneize Dirichlet BCs.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterList: Recognized keys of approximant parameters:
- 'POD': whether to compute POD of snapshots;
- 'R': rank for Galerkin projection;
- 'E': total number of derivatives current approximant relies upon.
POD: Whether to compute QR factorization of derivatives.
R: Rank for Galerkin projection.
E: Number of solution derivatives over which current approximant is
based upon.
uHF: High fidelity solution with wavenumber lastSolvedHF as numpy
complex vector.
lastSolvedHF: Wavenumber corresponding to last computed high fidelity
solution.
uApp: Last evaluated approximant as numpy complex vector.
ARBs: List of sparse matrices (in CSC format) representing RB
coefficients of linear system matrix wrt mu.
bRBs: List of numpy vectors representing RB coefficients of linear
system RHS wrt mu.
"""
def
__init__
(
self
,
HFEngine
:
HFEng
,
mu0
:
paramVal
=
0
,
approxParameters
:
DictAny
=
{},
homogeneized
:
bool
=
False
,
verbosity
:
int
=
10
,
timestamp
:
bool
=
True
):
self
.
_preInit
()
self
.
_addParametersToList
([
"R"
])
super
()
.
__init__
(
HFEngine
=
HFEngine
,
mu0
=
mu0
,
approxParameters
=
approxParameters
,
homogeneized
=
homogeneized
,
verbosity
=
verbosity
,
timestamp
=
timestamp
)
if
self
.
verbosity
>=
10
:
verbosityDepth
(
"INIT"
,
"Computing affine blocks of system."
,
timestamp
=
self
.
timestamp
)
if
self
.
verbosity
>=
10
:
verbosityDepth
(
"DEL"
,
"Done computing affine blocks."
,
timestamp
=
self
.
timestamp
)
self
.
_postInit
()
@property
def
approxParameters
(
self
):
"""
Value of approximant parameters. Its assignment may change M, N and S.
"""
return
self
.
_approxParameters
@approxParameters.setter
def
approxParameters
(
self
,
approxParams
):
approxParameters
=
purgeDict
(
approxParams
,
self
.
parameterList
,
dictname
=
self
.
name
()
+
".approxParameters"
,
baselevel
=
1
)
approxParametersCopy
=
purgeDict
(
approxParameters
,
[
"R"
],
True
,
True
,
baselevel
=
1
)
GenericCenteredApproximant
.
approxParameters
.
fset
(
self
,
approxParametersCopy
)
keyList
=
list
(
approxParameters
.
keys
())
if
"R"
in
keyList
:
self
.
R
=
approxParameters
[
"R"
]
else
:
self
.
R
=
self
.
E
+
1
@property
def
R
(
self
):
"""Value of R. Its assignment may change S."""
return
self
.
_R
@R.setter
def
R
(
self
,
R
):
if
R
<
0
:
raise
RROMPyException
(
"R must be non-negative."
)
self
.
_R
=
R
self
.
_approxParameters
[
"R"
]
=
self
.
R
if
hasattr
(
self
,
"_E"
)
and
self
.
E
+
1
<
self
.
R
:
RROMPyWarning
(
"Prescribed E is too small. Updating E to R - 1."
)
self
.
E
=
self
.
R
-
1
def
setupApprox
(
self
):
"""Setup RB system."""
if
self
.
checkComputedApprox
():
return
if
self
.
verbosity
>=
5
:
verbosityDepth
(
"INIT"
,
"Setting up {}."
.
format
(
self
.
name
()),
timestamp
=
self
.
timestamp
)
self
.
computeDerivatives
()
if
self
.
verbosity
>=
7
:
verbosityDepth
(
"INIT"
,
"Computing projection matrix."
,
timestamp
=
self
.
timestamp
)
if
self
.
POD
:
U
,
_
,
_
=
np
.
linalg
.
svd
(
self
.
samplingEngine
.
RPOD
[:
self
.
E
+
1
,
:
self
.
E
+
1
])
pMat
=
self
.
samplingEngine
.
samples
(
list
(
range
(
self
.
E
+
1
)))
.
dot
(
U
[:,
:
self
.
R
])
else
:
pMat
=
self
.
samplingEngine
.
samples
(
list
(
range
(
self
.
R
)))
if
self
.
trainedModel
is
None
:
self
.
trainedModel
=
tModel
()
self
.
trainedModel
.
verbosity
=
self
.
verbosity
self
.
trainedModel
.
timestamp
=
self
.
timestamp
data
=
TrainedModelData
(
self
.
trainedModel
.
name
(),
self
.
mu0
,
pMat
,
self
.
HFEngine
.
rescalingExp
)
data
.
thetaAs
=
self
.
HFEngine
.
affineWeightsA
(
self
.
mu0
)
data
.
thetabs
=
self
.
HFEngine
.
affineWeightsb
(
self
.
mu0
,
self
.
homogeneized
)
data
.
ARBs
,
data
.
bRBs
=
self
.
assembleReducedSystem
(
pMat
)
self
.
trainedModel
.
data
=
data
else
:
pMatOld
=
self
.
trainedModel
.
data
.
projMat
Sold
=
pMatOld
.
shape
[
1
]
ARBs
,
bRBs
=
self
.
assembleReducedSystem
(
pMat
(
list
(
range
(
Sold
,
pMat
.
shape
[
1
]))),
pMatOld
)
self
.
trainedModel
.
data
.
ARBs
=
ARBs
self
.
trainedModel
.
data
.
bRBs
=
bRBs
self
.
trainedModel
.
data
.
projMat
=
copy
(
pMat
)
if
self
.
verbosity
>=
7
:
verbosityDepth
(
"DEL"
,
"Done computing projection matrix."
,
timestamp
=
self
.
timestamp
)
self
.
trainedModel
.
data
.
approxParameters
=
copy
(
self
.
approxParameters
)
if
self
.
verbosity
>=
5
:
verbosityDepth
(
"DEL"
,
"Done setting up approximant."
,
timestamp
=
self
.
timestamp
)
def
assembleReducedSystem
(
self
,
pMat
:
sampList
=
None
,
pMatOld
:
sampList
=
None
)
\
->
Tuple
[
List
[
Np2D
],
List
[
Np1D
]]:
"""Build affine blocks of RB linear system through projections."""
if
pMat
is
None
:
self
.
setupApprox
()
ARBs
=
self
.
trainedModel
.
data
.
ARBs
bRBs
=
self
.
trainedModel
.
data
.
bRBs
else
:
if
self
.
verbosity
>=
10
:
verbosityDepth
(
"INIT"
,
"Projecting affine terms of HF model."
,
timestamp
=
self
.
timestamp
)
As
=
self
.
HFEngine
.
affineLinearSystemA
(
self
.
mu0
)
bs
=
self
.
HFEngine
.
affineLinearSystemb
(
self
.
mu0
,
self
.
homogeneized
)
ARBsOld
=
None
if
pMatOld
is
None
else
self
.
trainedModel
.
data
.
ARBs
bRBsOld
=
None
if
pMatOld
is
None
else
self
.
trainedModel
.
data
.
bRBs
ARBs
,
bRBs
=
projectAffineDecomposition
(
As
,
bs
,
pMat
,
ARBsOld
,
bRBsOld
,
pMatOld
)
if
self
.
verbosity
>=
10
:
verbosityDepth
(
"DEL"
,
"Done projecting affine terms."
,
timestamp
=
self
.
timestamp
)
return
ARBs
,
bRBs
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