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rb_distributed.py
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Thu, May 9, 11:38
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R6746 RationalROMPy
rb_distributed.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_distributed_approximant
import
GenericDistributedApproximant
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
,
List
,
Tuple
,
DictAny
,
HFEng
,
paramVal
)
from
rrompy.utilities.base
import
purgeDict
,
verbosityDepth
from
rrompy.utilities.exception_manager
import
RROMPyWarning
,
RROMPyException
__all__
=
[
'RBDistributed'
]
class
RBDistributed
(
GenericDistributedApproximant
):
"""
ROM RB approximant computation for parametric problems.
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:
- 'muBounds': list of bounds for parameter values; defaults to
[0, 1];
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator; defaults to uniform sampler on
muBounds;
- 'R': rank for Galerkin projection; defaults to prod(S).
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.
mus: Array of snapshot parameters.
homogeneized: Whether to homogeneize Dirichlet BCs.
approxRadius: Dummy radius of approximant (i.e. distance from mu0 to
farthest sample point).
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;
- 'muBounds': list of bounds for parameter values;
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator;
- 'R': rank for Galerkin projection.
extraApproxParameters: List of approxParameters keys in addition to
mother class's.
POD: Whether to compute POD of snapshots.
muBounds: list of bounds for parameter values.
S: Number of solution snapshots over which current approximant is
based upon.
sampler: Sample point generator.
R: Rank for Galerkin projection.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uAppReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApp as sampleList.
lastSolvedAppReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApp: Approximate solution(s) with parameter(s) lastSolvedApp as
sampleList.
lastSolvedApp: Parameter(s) corresponding to last computed approximate
solution(s) as parameterList.
As: List of sparse matrices (in CSC format) representing coefficients
of linear system matrix wrt theta(mu).
bs: List of numpy vectors representing coefficients of linear system
RHS wrt theta(mu).
thetaAs: List of callables representing coefficients of linear system
matrix wrt mu.
thetabs: List of callables representing coefficients of linear system
RHS wrt mu.
ARBs: List of sparse matrices (in CSC format) representing coefficients
of compressed linear system matrix wrt theta(mu).
bRBs: List of numpy vectors representing coefficients of compressed
linear system RHS wrt theta(mu).
"""
def
__init__
(
self
,
HFEngine
:
HFEng
,
mu0
:
paramVal
=
None
,
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
)
self
.
As
=
self
.
HFEngine
.
affineLinearSystemA
(
self
.
mu0
)
self
.
bs
=
self
.
HFEngine
.
affineLinearSystemb
(
self
.
mu0
,
self
.
homogeneized
)
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
)
GenericDistributedApproximant
.
approxParameters
.
fset
(
self
,
approxParametersCopy
)
keyList
=
list
(
approxParameters
.
keys
())
if
"R"
in
keyList
:
self
.
R
=
approxParameters
[
"R"
]
elif
hasattr
(
self
,
"_R"
)
and
self
.
_R
is
not
None
:
self
.
R
=
self
.
R
else
:
self
.
R
=
np
.
prod
(
self
.
S
)
@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
,
"_S"
)
and
np
.
prod
(
self
.
S
)
<
self
.
R
:
RROMPyWarning
(
"Prescribed S is too small. Decreasing R."
)
self
.
R
=
np
.
prod
(
self
.
S
)
def
setupApprox
(
self
):
"""Compute RB projection matrix."""
if
self
.
checkComputedApprox
():
return
if
self
.
verbosity
>=
5
:
verbosityDepth
(
"INIT"
,
"Setting up {}."
.
format
(
self
.
name
()),
timestamp
=
self
.
timestamp
)
self
.
computeSnapshots
()
if
self
.
verbosity
>=
7
:
verbosityDepth
(
"INIT"
,
"Computing projection matrix."
,
timestamp
=
self
.
timestamp
)
if
self
.
POD
:
U
,
_
,
_
=
np
.
linalg
.
svd
(
self
.
samplingEngine
.
RPOD
,
full_matrices
=
False
)
pMat
=
self
.
samplingEngine
.
samples
.
dot
(
U
[:,
:
self
.
R
])
else
:
pMat
=
self
.
samplingEngine
.
samples
[:
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
)
data
.
mus
=
copy
(
self
.
mus
)
self
.
trainedModel
.
data
=
data
else
:
self
.
trainedModel
=
self
.
trainedModel
pMatOld
=
self
.
trainedModel
.
data
.
projMat
Sold
=
pMatOld
.
shape
[
1
]
ARBs
,
bRBs
=
self
.
assembleReducedSystem
(
pMat
[:,
Sold
:],
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
:
Np2D
=
None
,
pMatOld
:
Np2D
=
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
)
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
(
self
.
As
,
self
.
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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