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RROMPy/rrompy/reduction_methods/pivoting/rb_distributed_pivoted_pole_matching.py
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rb_distributed_pivoted_pole_matching.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
from .rb_distributed_pivoted import RBDistributedPivoted
from rrompy.reduction_methods.trained_model import (TrainedModelPivotedData,
TrainedModelPivotedRBPoleMatching as tModel)
from rrompy.utilities.base.types import HFEng, DictAny, ListAny, paramVal
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert)
__all__ = ['RBDistributedPivotedPoleMatching']
class RBDistributedPivotedPoleMatching(RBDistributedPivoted):
"""
ROM pivoted RB approximant computation for parametric problems with pole
matching.
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
directionPivot(optional): Pivot components. 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;
- 'matchingWeight': weight for pole matching optimization; defaults
to 1;
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator;
- 'R': rank for pivot Galerkin projection; defaults to prod(S);
- 'PODTolerance': tolerance for pivot snapshots POD; defaults to
-1;
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation; allowed values include 'MONOMIAL', 'CHEBYSHEV'
and 'LEGENDRE'; defaults to 'MONOMIAL';
- 'MMarginal': degree of marginal interpolant; defaults to 0;
- 'radialBasisMarginal': radial basis family for marginal
interpolant; defaults to 0, i.e. no radial basis;
- 'radialBasisWeightsMarginal': radial basis weights for marginal
interpolant; defaults to 0, i.e. identity;
- 'interpRcondMarginal': tolerance for marginal interpolation;
defaults to None.
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.
directionPivot: Pivot components.
mus: Array of snapshot parameters.
musPivot: Array of pivot snapshot parameters.
musMarginal: Array of marginal snapshot parameters.
homogeneized: Whether to homogeneize Dirichlet BCs.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': whether to compute POD of snapshots;
- 'matchingWeight': weight for pole matching optimization;
- 'R': rank for Galerkin projection;
- 'PODTolerance': tolerance for snapshots POD;
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation;
- 'MMarginal': degree of marginal interpolant;
- 'radialBasisMarginal': radial basis family for marginal
interpolant;
- 'radialBasisWeightsMarginal': radial basis weights for marginal
interpolant;
- 'interpRcondMarginal': tolerance for marginal interpolation.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator.
POD: Whether to compute POD of snapshots.
matchingWeight: Weight for pole matching optimization.
S: Total number of pivot samples current approximant relies upon.
samplerPivot: Pivot sample point generator.
SMarginal: Total number of marginal samples current approximant relies
upon.
samplerMarginal: Marginal sample point generator.
R: Rank for Galerkin projection.
PODTolerance: Tolerance for pivot snapshots POD.
polybasisMarginal: Type of polynomial basis for marginal interpolation.
MMarginal: Degree of marginal interpolant.
radialBasisMarginal: Radial basis family for marginal interpolant.
radialBasisWeightsMarginal: Radial basis weights for marginal
interpolant.
interpRcondMarginal: Tolerance for marginal interpolation.
muBoundsPivot: list of bounds for pivot parameter values.
muBoundsMarginal: list of bounds for marginal parameter values.
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.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: 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.
bs: List of numpy vectors representing coefficients of linear system
RHS.
ARBs: List of sparse matrices (in CSC format) representing coefficients
of compressed linear system matrix.
bRBs: List of numpy vectors representing coefficients of compressed
linear system RHS.
"""
def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
directionPivot : ListAny = [0],
approxParameters : DictAny = {}, homogeneized : bool = False,
verbosity : int = 10, timestamp : bool = True):
self._preInit()
if len(directionPivot) > 1:
raise RROMPyException(("Exactly 1 pivot parameter allowed in pole "
"matching."))
self._addParametersToList(["matchingWeight"], [1])
super().__init__(HFEngine = HFEngine, mu0 = mu0,
directionPivot = directionPivot,
approxParameters = approxParameters,
homogeneized = homogeneized,
verbosity = verbosity, timestamp = timestamp)
self._postInit()
@property
def matchingWeight(self):
"""Value of matchingWeight."""
return self._matchingWeight
@matchingWeight.setter
def matchingWeight(self, matchingWeight):
self._matchingWeight = matchingWeight
self._approxParameters["matchingWeight"] = self.matchingWeight
def setupApprox(self):
"""Compute RB projection matrix."""
if self.checkComputedApprox():
return
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
vbMng(self, "INIT", "Setting up {}.".format(self.name()), 5)
self.computeScaleFactor()
self.computeSnapshots()
self._setupAffineBlocks()
pMat = self._setupProjectionMatrix()
ARBsList, bRBsList, pList = self.assembleReducedSystemMarginal(pMat)
if self.trainedModel is None:
self.trainedModel = tModel()
self.trainedModel.verbosity = self.verbosity
self.trainedModel.timestamp = self.timestamp
data = TrainedModelPivotedData(self.trainedModel.name(), self.mu0,
pList, self.HFEngine.rescalingExp,
self.directionPivot,
self.scaleFactor)
data.musPivot = copy(self.musPivot)
data.musMarginal = copy(self.musMarginal)
self.trainedModel.data = data
else:
self.trainedModel = self.trainedModel
self.trainedModel.data.projMat = copy(pList)
self.trainedModel.data.marginalInterp = self._setupMarginalInterp()
self.trainedModel.data.ARBsList = ARBsList
self.trainedModel.data.bRBsList = bRBsList
vbMng(self, "INIT", "Matching poles.", 10)
self.trainedModel.initializeFromAffine(self.HFEngine,
self.matchingWeight, self.POD)
vbMng(self, "DEL", "Done matching poles.", 10)
self.trainedModel.data.approxParameters = copy(self.approxParameters)
vbMng(self, "DEL", "Done setting up approximant.", 5)
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