pymor.reductors package¶
Submodules¶
basic module¶

class
pymor.reductors.basic.
DelayLTIPGReductor
(fom, W, V, E_biorthonormal=False)[source]¶ Bases:
pymor.reductors.basic.ProjectionBasedReductor
PetrovGalerkin projection of an
LinearDelayModel
.Parameters
Methods
build_rom
,extend_basis
,project_operators
,project_operators_to_subbasis
,reconstruct
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,reduce
Attributes

class
pymor.reductors.basic.
InstationaryRBReductor
(fom, RB=None, product=None, initial_data_product=None, product_is_mass=False, check_orthonormality=None, check_tol=None)[source]¶ Bases:
pymor.reductors.basic.ProjectionBasedReductor
Galerkin projection of an
InstationaryModel
.Parameters
 fom
The full order
Model
to reduce. RB
The basis of the reduced space onto which to project. If
None
an empty basis is used. product
Inner product
Operator
w.r.t. whichRB
is orthonormalized. IfNone
, the the Euclidean inner product is used. initial_data_product
Inner product
Operator
w.r.t. which theinitial_data
offom
is orthogonally projected. IfNone
, the Euclidean inner product is used. product_is_mass
If
True
, no mass matrix for the reducedModel
is assembled. Set toTrue
ifRB
is orthonormal w.r.t. themass
matrix offom
. check_orthonormality
 check_tol
Methods
build_rom
,project_operators
,project_operators_to_subbasis
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,extend_basis
,reconstruct
,reduce
Attributes

class
pymor.reductors.basic.
LTIPGReductor
(fom, W, V, E_biorthonormal=False)[source]¶ Bases:
pymor.reductors.basic.ProjectionBasedReductor
PetrovGalerkin projection of an
LTIModel
.Parameters
Methods
build_rom
,extend_basis
,project_operators
,project_operators_to_subbasis
,reconstruct
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,reduce
Attributes

class
pymor.reductors.basic.
ProjectionBasedReductor
(**wrapper_kwargs)[source]¶ Bases:
pymor.core.base.BasicObject
Generic projection based reductor.
Parameters
 fom
The full order
Model
to reduce. bases
A dict of
VectorArrays
of basis vectors. products
A dict of inner product
Operators
w.r.t. which the corresponding bases are orthonormalized. A value ofNone
corresponds to orthonormalization of the basis w.r.t. the Euclidean inner product. check_orthonormality
If
True
, check if bases which have a corresponding entry in theproducts
dict are orthonormal w.r.t. the given inner product. After eachbasis extension
, orthonormality is checked again. check_tol
If
check_orthonormality
isTrue
, the numerical tolerance with which the checks are performed.
Methods
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,build_rom
,extend_basis
,project_operators
,project_operators_to_subbasis
,reconstruct
,reduce
Attributes

class
pymor.reductors.basic.
SOLTIPGReductor
(fom, W, V, M_biorthonormal=False)[source]¶ Bases:
pymor.reductors.basic.ProjectionBasedReductor
PetrovGalerkin projection of an
SecondOrderModel
.Parameters
Methods
build_rom
,extend_basis
,project_operators
,project_operators_to_subbasis
,reconstruct
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,reduce
Attributes

class
pymor.reductors.basic.
StationaryRBReductor
(fom, RB=None, product=None, check_orthonormality=None, check_tol=None)[source]¶ Bases:
pymor.reductors.basic.ProjectionBasedReductor
Galerkin projection of a
StationaryModel
.Parameters
Methods
build_rom
,project_operators
,project_operators_to_subbasis
assemble_error_estimator
,assemble_error_estimator_for_subbasis
,extend_basis
,reconstruct
,reduce
Attributes

pymor.reductors.basic.
extend_basis
(U, basis, product=None, method='gram_schmidt', pod_modes=1, pod_orthonormalize=True, copy_U=True)[source]¶
bt module¶

class
pymor.reductors.bt.
BRBTReductor
(fom, gamma=1, mu=None, solver_options=None)[source]¶ Bases:
pymor.reductors.bt.GenericBTReductor
Bounded Real (BR) Balanced Truncation reductor.
See [A05] (Section 7.5.3) and [OJ88].
Parameters
 fom
The fullorder
LTIModel
to reduce. gamma
Upper bound for the \(\mathcal{H}_\infty\)norm.
 mu
 solver_options
The solver options to use to solve the positive Riccati equations.
Methods
Attributes

class
pymor.reductors.bt.
BTReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.bt.GenericBTReductor
Standard (Lyapunov) Balanced Truncation reductor.
See Section 7.3 in [A05].
Parameters
 fom
The fullorder
LTIModel
to reduce. mu
Methods
Attributes

class
pymor.reductors.bt.
GenericBTReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Generic Balanced Truncation reductor.
Parameters
 fom
The fullorder
LTIModel
to reduce. mu
Methods
Attributes

reduce
(r=None, tol=None, projection='bfsr')[source]¶ Generic Balanced Truncation.
Parameters
 r
Order of the reduced model if
tol
isNone
, maximum order iftol
is specified. tol
Tolerance for the error bound if
r
isNone
. projection
Projection method used:
'sr'
: square root method'bfsr'
: balancingfree square root method (default, since it avoids scaling by singular values and orthogonalizes the projection matrices, which might make it more accurate than the square root method)'biorth'
: like the balancingfree square root method, except it biorthogonalizes the projection matrices (usinggram_schmidt_biorth
)
Returns
 rom
Reducedorder model.

class
pymor.reductors.bt.
LQGBTReductor
(fom, mu=None, solver_options=None)[source]¶ Bases:
pymor.reductors.bt.GenericBTReductor
Linear Quadratic Gaussian (LQG) Balanced Truncation reductor.
See Section 3 in [MG91].
Parameters
 fom
The fullorder
LTIModel
to reduce. mu
 solver_options
The solver options to use to solve the Riccati equations.
Methods
Attributes
coercive module¶

class
pymor.reductors.coercive.
CoerciveRBEstimator
(*args, **kwargs)[source]¶ Bases:
pymor.core.base.ImmutableObject
Instantiated by
CoerciveRBReductor
.Not to be used directly.
Methods
estimate
,estimate_error
,restricted_to_subbasis
Attributes

class
pymor.reductors.coercive.
CoerciveRBReductor
(fom, RB=None, product=None, coercivity_estimator=None, check_orthonormality=None, check_tol=None)[source]¶ Bases:
pymor.reductors.basic.StationaryRBReductor
Reduced Basis reductor for
StationaryModels
with coercive linear operator.The only addition to
StationaryRBReductor
is an error estimator which evaluates the dual norm of the residual with respect to a given inner product. For the reduction of the residual we useResidualReductor
for improved numerical stability [BEOR14].Parameters
 fom
The
Model
which is to be reduced. RB
VectorArray
containing the reduced basis on which to project. product
Inner product for the orthonormalization of
RB
, the projection of theOperators
given byvector_ranged_operators
and for the computation of Riesz representatives of the residual. IfNone
, the Euclidean product is used. coercivity_estimator
None
or aParameterFunctional
returning a lower bound for the coercivity constant of the given problem. Note that the computed error estimate is only guaranteed to be an upper bound for the error when an appropriate coercivity estimate is specified.
Methods
assemble_error_estimator
,assemble_error_estimator_for_subbasis
build_rom
,project_operators
,project_operators_to_subbasis
extend_basis
,reconstruct
,reduce
Attributes

class
pymor.reductors.coercive.
SimpleCoerciveRBEstimator
(*args, **kwargs)[source]¶ Bases:
pymor.core.base.ImmutableObject
Instantiated by
SimpleCoerciveRBReductor
.Not to be used directly.
Methods
estimate
,estimate_error
,restricted_to_subbasis
Attributes

class
pymor.reductors.coercive.
SimpleCoerciveRBReductor
(fom, RB=None, product=None, coercivity_estimator=None, check_orthonormality=None, check_tol=None)[source]¶ Bases:
pymor.reductors.basic.StationaryRBReductor
Reductor for linear
StationaryModels
with affinely decomposed operator and rhs.Note
The reductor
CoerciveRBReductor
can be used for arbitrary coerciveStationaryModels
and offers an improved error estimator with better numerical stability.The only addition is to
StationaryRBReductor
is an error estimator, which evaluates the norm of the residual with respect to a given inner product.Parameters
 fom
The
Model
which is to be reduced. RB
VectorArray
containing the reduced basis on which to project. product
Inner product for the orthonormalization of
RB
, the projection of theOperators
given byvector_ranged_operators
and for the computation of Riesz representatives of the residual. IfNone
, the Euclidean product is used. coercivity_estimator
None
or aParameterFunctional
returning a lower bound for the coercivity constant of the given problem. Note that the computed error estimate is only guaranteed to be an upper bound for the error when an appropriate coercivity estimate is specified.
Methods
assemble_error_estimator
,assemble_error_estimator_for_subbasis
build_rom
,project_operators
,project_operators_to_subbasis
extend_basis
,reconstruct
,reduce
Attributes
h2 module¶
Reductors based on H2norm.

class
pymor.reductors.h2.
GenericIRKAReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Generic IRKA related reductor.
Parameters
 fom
The fullorder
Model
to reduce. mu
Attributes

class
pymor.reductors.h2.
IRKAReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.h2.GenericIRKAReductor
Iterative Rational Krylov Algorithm reductor.
Parameters
 fom
The fullorder
LTIModel
to reduce. mu
Methods
Attributes

reduce
(rom0_params, tol=0.0001, maxit=100, num_prev=1, force_sigma_in_rhp=False, projection='orth', conv_crit='sigma', compute_errors=False)[source]¶ Reduce using IRKA.
See [GAB08] (Algorithm 4.1) and [ABG10] (Algorithm 1).
Parameters
 rom0_params
Can be:
order of the reduced model (a positive integer),
initial interpolation points (a 1D
NumPy array
),dict with
'sigma'
,'b'
,'c'
as keys mapping to initial interpolation points (a 1DNumPy array
), right tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
), and left tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
),initial reducedorder model (
LTIModel
).
If the order of reduced model is given, initial interpolation data is generated randomly.
 tol
Tolerance for the convergence criterion.
 maxit
Maximum number of iterations.
 num_prev
Number of previous iterations to compare the current iteration to. Larger number can avoid occasional cyclic behavior of IRKA.
 force_sigma_in_rhp
If
False
, new interpolation are reflections of the current reducedorder model’s poles. Otherwise, only poles in the left halfplane are reflected. projection
Projection method:
'orth'
: projection matrices are orthogonalized with respect to the Euclidean inner product'biorth'
: projection matrices are biorthogolized with respect to the E product'arnoldi'
: projection matrices are orthogonalized using the Arnoldi process (available only for SISO systems).
 conv_crit
Convergence criterion:
'sigma'
: relative change in interpolation points'h2'
: relative \(\mathcal{H}_2\) distance of reducedorder models
 compute_errors
Should the relative \(\mathcal{H}_2\)errors of intermediate reducedorder models be computed.
Warning
Computing \(\mathcal{H}_2\)errors is expensive. Use this option only if necessary.
Returns
 rom
Reduced
LTIModel
model.

class
pymor.reductors.h2.
OneSidedIRKAReductor
(fom, version, mu=None)[source]¶ Bases:
pymor.reductors.h2.GenericIRKAReductor
OneSided Iterative Rational Krylov Algorithm reductor.
Parameters
 fom
The fullorder
LTIModel
to reduce. version
Version of the onesided IRKA:
'V'
: Galerkin projection using the input Krylov subspace,'W'
: Galerkin projection using the output Krylov subspace.
 mu
Methods
Attributes

reduce
(rom0_params, tol=0.0001, maxit=100, num_prev=1, force_sigma_in_rhp=False, projection='orth', conv_crit='sigma', compute_errors=False)[source]¶ Reduce using onesided IRKA.
Parameters
 rom0_params
Can be:
order of the reduced model (a positive integer),
initial interpolation points (a 1D
NumPy array
),dict with
'sigma'
,'b'
,'c'
as keys mapping to initial interpolation points (a 1DNumPy array
), right tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
), and left tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
),initial reducedorder model (
LTIModel
).
If the order of reduced model is given, initial interpolation data is generated randomly.
 tol
Tolerance for the largest change in interpolation points.
 maxit
Maximum number of iterations.
 num_prev
Number of previous iterations to compare the current iteration to. A larger number can avoid occasional cyclic behavior.
 force_sigma_in_rhp
If
False
, new interpolation are reflections of the current reducedorder model’s poles. Otherwise, only poles in the left halfplane are reflected. projection
Projection method:
'orth'
: projection matrix is orthogonalized with respect to the Euclidean inner product,'Eorth'
: projection matrix is orthogonalized with respect to the E product.
 conv_crit
Convergence criterion:
'sigma'
: relative change in interpolation points,'h2'
: relative \(\mathcal{H}_2\) distance of reducedorder models.
 compute_errors
Should the relative \(\mathcal{H}_2\)errors of intermediate reducedorder models be computed.
Warning
Computing \(\mathcal{H}_2\)errors is expensive. Use this option only if necessary.
Returns
 rom
Reduced
LTIModel
model.

class
pymor.reductors.h2.
TFIRKAReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.h2.GenericIRKAReductor
Realizationindependent IRKA reductor.
See [BG12].
Parameters
 fom
The fullorder
Model
witheval_tf
andeval_dtf
methods. mu
Attributes

reduce
(rom0_params, tol=0.0001, maxit=100, num_prev=1, force_sigma_in_rhp=False, conv_crit='sigma', compute_errors=False)[source]¶ Reduce using TFIRKA.
Parameters
 rom0_params
Can be:
order of the reduced model (a positive integer),
initial interpolation points (a 1D
NumPy array
),dict with
'sigma'
,'b'
,'c'
as keys mapping to initial interpolation points (a 1DNumPy array
), right tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
), and left tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
),initial reducedorder model (
LTIModel
).
If the order of reduced model is given, initial interpolation data is generated randomly.
 tol
Tolerance for the convergence criterion.
 maxit
Maximum number of iterations.
 num_prev
Number of previous iterations to compare the current iteration to. Larger number can avoid occasional cyclic behavior of TFIRKA.
 force_sigma_in_rhp
If
False
, new interpolation are reflections of the current reducedorder model’s poles. Otherwise, only poles in the left halfplane are reflected. conv_crit
Convergence criterion:
'sigma'
: relative change in interpolation points'h2'
: relative \(\mathcal{H}_2\) distance of reducedorder models
 compute_errors
Should the relative \(\mathcal{H}_2\)errors of intermediate reducedorder models be computed.
Warning
Computing \(\mathcal{H}_2\)errors is expensive. Use this option only if necessary.
Returns
 rom
Reduced
LTIModel
model.

class
pymor.reductors.h2.
TSIAReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.h2.GenericIRKAReductor
TwoSided Iteration Algorithm reductor.
Parameters
 fom
The fullorder
LTIModel
to reduce. mu
Methods
Attributes

reduce
(rom0_params, tol=0.0001, maxit=100, num_prev=1, projection='orth', conv_crit='sigma', compute_errors=False)[source]¶ Reduce using TSIA.
See [XZ11] (Algorithm 1) and [BKS11].
In exact arithmetic, TSIA is equivalent to IRKA (under some assumptions on the poles of the reduced model). The main difference in implementation is that TSIA computes the Schur decomposition of the reduced matrices, while IRKA computes the eigenvalue decomposition. Therefore, TSIA might behave better for nonnormal reduced matrices.
Parameters
 rom0_params
Can be:
order of the reduced model (a positive integer),
initial interpolation points (a 1D
NumPy array
),dict with
'sigma'
,'b'
,'c'
as keys mapping to initial interpolation points (a 1DNumPy array
), right tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
), and left tangential directions (NumPy array
of shape(len(sigma), fom.dim_input)
),initial reducedorder model (
LTIModel
).
If the order of reduced model is given, initial interpolation data is generated randomly.
 tol
Tolerance for the convergence criterion.
 maxit
Maximum number of iterations.
 num_prev
Number of previous iterations to compare the current iteration to. Larger number can avoid occasional cyclic behavior of TSIA.
 projection
Projection method:
'orth'
: projection matrices are orthogonalized with respect to the Euclidean inner product'biorth'
: projection matrices are biorthogolized with respect to the E product
 conv_crit
Convergence criterion:
'sigma'
: relative change in interpolation points'h2'
: relative \(\mathcal{H}_2\) distance of reducedorder models
 compute_errors
Should the relative \(\mathcal{H}_2\)errors of intermediate reducedorder models be computed.
Warning
Computing \(\mathcal{H}_2\)errors is expensive. Use this option only if necessary.
Returns
 rom
Reduced
LTIModel
.

pymor.reductors.h2.
_lti_to_poles_b_c
(rom)[source]¶ Compute poles and residues.
Parameters
 rom
Reduced
LTIModel
(consisting ofNumpyMatrixOperators
).
Returns
 poles
1D
NumPy array
of poles. b
NumPy array
of shape(rom.order, rom.dim_input)
. c
NumPy array
of shape(rom.order, rom.dim_output)
.

pymor.reductors.h2.
_poles_b_c_to_lti
(poles, b, c)[source]¶ Create an
LTIModel
from poles and residue rank1 factors.Returns an
LTIModel
with real matrices such that its transfer function is\[\sum_{i = 1}^r \frac{c_i b_i^T}{s  \lambda_i}\]where \(\lambda_i, b_i, c_i\) are the poles and residue rank1 factors.
Parameters
 poles
Sequence of poles.
 b
NumPy array
of shape(rom.order, rom.dim_input)
. c
NumPy array
of shape(rom.order, rom.dim_output)
.
Returns
interpolation module¶

class
pymor.reductors.interpolation.
DelayBHIReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.interpolation.GenericBHIReductor
Bitangential Hermite interpolation for
LinearDelayModels
.Parameters
 fom
The fullorder
LinearDelayModel
to reduce. mu
Attributes

_PGReductor
¶

class
pymor.reductors.interpolation.
GenericBHIReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Generic bitangential Hermite interpolation reductor.
This is a generic reductor for reducing any linear
InputStateOutputModel
with the transfer function which can be written in the generalized coprime factorization \(H(s) = \mathcal{C}(s) \mathcal{K}(s)^{1} \mathcal{B}(s)\) as in [BG09]. The interpolation here is limited to only up to the first derivative. Interpolation points are assumed to be pairwise distinct.In particular, given interpolation points \(\sigma_i\), right tangential directions \(b_i\), and left tangential directions \(c_i\), for \(i = 1, 2, \ldots, r\), which are closed under conjugation (if \(\sigma_i\) is real, then so are \(b_i\) and \(c_i\); if \(\sigma_i\) is complex, there is \(\sigma_j\) such that \(\sigma_j = \overline{\sigma_i}\), \(b_j = \overline{b_i}\), \(c_j = \overline{c_i}\)), this reductor finds a transfer function \(\hat{H}\) such that
\[\begin{split}H(\sigma_i) b_i & = \hat{H}(\sigma_i) b_i, \\ c_i^T H(\sigma_i) & = c_i^T \hat{H}(\sigma_i) b_i, \  \widehat{y}\ c_i^T H'(\sigma_i) b_i & = c_i^T \hat{H}'(\sigma_i) b_i,\end{split}\]for all \(i = 1, 2, \ldots, r\).
Parameters
 fom
The fullorder
Model
to reduce. mu
Attributes

_PGReductor
¶

reduce
(sigma, b, c, projection='orth')[source]¶ Bitangential Hermite interpolation.
Parameters
 sigma
Interpolation points (closed under conjugation), sequence of length
r
. b
Right tangential directions,
NumPy array
of shape(r, fom.dim_input)
. c
Left tangential directions,
NumPy array
of shape(r, fom.dim_output)
. projection
Projection method:
'orth'
: projection matrices are orthogonalized with respect to the Euclidean inner product'biorth'
: projection matrices are biorthogolized with respect to the E product
Returns
 rom
Reducedorder model.

class
pymor.reductors.interpolation.
LTIBHIReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.interpolation.GenericBHIReductor
Bitangential Hermite interpolation for
LTIModels
.Parameters
 fom
The fullorder
LTIModel
to reduce. mu
Methods
Attributes

_PGReductor
¶ alias of
pymor.reductors.basic.LTIPGReductor

reduce
(sigma, b, c, projection='orth')[source]¶ Bitangential Hermite interpolation.
Parameters
 sigma
Interpolation points (closed under conjugation), sequence of length
r
. b
Right tangential directions,
NumPy array
of shape(r, fom.dim_input)
. c
Left tangential directions,
NumPy array
of shape(r, fom.dim_output)
. projection
Projection method:
'orth'
: projection matrices are orthogonalized with respect to the Euclidean inner product'biorth'
: projection matrices are biorthogolized with respect to the E product'arnoldi'
: projection matrices are orthogonalized using the rational Arnoldi process (available only for SISO systems).
Returns
 rom
Reducedorder model.

class
pymor.reductors.interpolation.
SOBHIReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.interpolation.GenericBHIReductor
Bitangential Hermite interpolation for
SecondOrderModels
.Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

_PGReductor
¶

class
pymor.reductors.interpolation.
TFBHIReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Loewner bitangential Hermite interpolation reductor.
See [BG12].
Parameters
 fom
The
Model
witheval_tf
andeval_dtf
methods. mu
Attributes

reduce
(sigma, b, c)[source]¶ Realizationindependent tangential Hermite interpolation.
Parameters
 sigma
Interpolation points (closed under conjugation), sequence of length
r
. b
Right tangential directions,
NumPy array
of shape(r, fom.dim_input)
. c
Left tangential directions,
NumPy array
of shape(r, fom.dim_output)
.
Returns
 lti
The reducedorder
LTIModel
interpolating the transfer function offom
.
neural_network module¶

class
pymor.reductors.neural_network.
CustomDataset
(training_data)[source]¶ Bases:
torch.utils.data.dataset.Dataset
Class that represents the dataset to use in PyTorch.
Parameters
 training_data
Set of training parameters and the respective coefficients of the solution in the reduced basis.

class
pymor.reductors.neural_network.
EarlyStoppingScheduler
(size_training_validation_set, patience=10, delta=0.0)[source]¶ Bases:
pymor.core.base.BasicObject
Class for performing early stopping in training of neural networks.
If the validation loss does not decrease over a certain amount of epochs, the training should be aborted to avoid overfitting the training data. This class implements an early stopping scheduler that recommends to stop the training process if the validation loss did not decrease by at least
delta
overpatience
epochs.Parameters
 size_training_validation_set
Size of both, training and validation set together.
 patience
Number of epochs of nondecreasing validation loss allowed, before early stopping the training process.
 delta
Minimal amount of decrease in the validation loss that is required to reset the counter of nondecreasing epochs.
Attributes

__call__
(losses, neural_network=None)[source]¶ Returns
True
if early stopping of training is suggested.Parameters
 losses
Dictionary of losses on the validation and the training set in the current epoch.
 neural_network
Neural network that produces the current validation loss.
Returns
True
if early stopping is suggested,False
otherwise.

class
pymor.reductors.neural_network.
NeuralNetworkInstationaryReductor
(fom, training_set, validation_set=None, validation_ratio=0.1, basis_size=None, rtol=0.0, atol=0.0, l2_err=0.0, pod_params=None, ann_mse='like_basis')[source]¶ Bases:
pymor.reductors.neural_network.NeuralNetworkReductor
Reduced Basis reductor for instationary problems relying on artificial neural networks.
This is a reductor that constructs a reduced basis using proper orthogonal decomposition and trains a neural network that approximates the mapping from parameter and time space to coefficients of the fullorder solution in the reduced basis. The approach is described in [WHR19].
Parameters
 fom
The fullorder
Model
to reduce. training_set
Set of
parameter values
to use for POD and training of the neural network. validation_set
Set of
parameter values
to use for validation in the training of the neural network. validation_ratio
Fraction of the training set to use for validation in the training of the neural network (only used if no validation set is provided).
 basis_size
Desired size of the reduced basis. If
None
, rtol, atol or l2_err must be provided. rtol
Relative tolerance the basis should guarantee on the training set.
 atol
Absolute tolerance the basis should guarantee on the training set.
 l2_err
L2approximation error the basis should not exceed on the training set.
 pod_params
Dict of additional parameters for the PODmethod.
 ann_mse
If
'like_basis'
, the mean squared error of the neural network on the training set should not exceed the error of projecting onto the basis. IfNone
, the neural network with smallest validation error is used to build the ROM. If a tolerance is prescribed, the mean squared error of the neural network on the training set should not exceed this threshold. Training is interrupted if a neural network that undercuts the error tolerance is found.
Methods
Attributes

_compute_layers_sizes
(hidden_layers)[source]¶ Compute the number of neurons in the layers of the neural network (make sure to increase the input dimension to account for the time).

class
pymor.reductors.neural_network.
NeuralNetworkReductor
(fom, training_set, validation_set=None, validation_ratio=0.1, basis_size=None, rtol=0.0, atol=0.0, l2_err=0.0, pod_params=None, ann_mse='like_basis')[source]¶ Bases:
pymor.core.base.BasicObject
Reduced Basis reductor relying on artificial neural networks.
This is a reductor that constructs a reduced basis using proper orthogonal decomposition and trains a neural network that approximates the mapping from parameter space to coefficients of the fullorder solution in the reduced basis. The approach is described in [HU18].
Parameters
 fom
The fullorder
Model
to reduce. training_set
Set of
parameter values
to use for POD and training of the neural network. validation_set
Set of
parameter values
to use for validation in the training of the neural network. validation_ratio
Fraction of the training set to use for validation in the training of the neural network (only used if no validation set is provided).
 basis_size
Desired size of the reduced basis. If
None
, rtol, atol or l2_err must be provided. rtol
Relative tolerance the basis should guarantee on the training set.
 atol
Absolute tolerance the basis should guarantee on the training set.
 l2_err
L2approximation error the basis should not exceed on the training set.
 pod_params
Dict of additional parameters for the PODmethod.
 ann_mse
If
'like_basis'
, the mean squared error of the neural network on the training set should not exceed the error of projecting onto the basis. IfNone
, the neural network with smallest validation error is used to build the ROM. If a tolerance is prescribed, the mean squared error of the neural network on the training set should not exceed this threshold. Training is interrupted if a neural network that undercuts the error tolerance is found.
Methods
Attributes

_compute_layers_sizes
(hidden_layers)[source]¶ Compute the number of neurons in the layers of the neural network.

_compute_sample
(mu, u, reduced_basis)[source]¶ Transform parameter and corresponding solution to tensors.

_train
(layers, activation_function, optimizer, epochs, batch_size, learning_rate)[source]¶ Perform a single training iteration and return the resulting neural network.

reduce
(hidden_layers='[(N+P)*3, (N+P)*3]', activation_function=<builtin method tanh of type object>, optimizer=<class 'torch.optim.lbfgs.LBFGS'>, epochs=1000, batch_size=20, learning_rate=1.0, restarts=10, seed=0)[source]¶ Reduce by training artificial neural networks.
Parameters
 hidden_layers
Number of neurons in the hidden layers. Can either be fixed or a Python expression string depending on the reduced basis size
N
and the total dimension of theParameters
P
. activation_function
Activation function to use between the hidden layers.
 optimizer
Algorithm to use as optimizer during training.
 epochs
Maximum number of epochs for training.
 batch_size
Batch size to use if optimizer allows minibatching.
 learning_rate
Step size to use in each optimization step.
 restarts
Number of restarts of the training algorithm. Since the training results highly depend on the initial starting point, i.e. the initial weights and biases, it is advisable to train multiple neural networks by starting with different initial values and choose that one performing best on the validation set.
 seed
Seed to use for various functions in PyTorch. Using a fixed seed, it is possible to reproduce former results.
Returns
 rom
Reducedorder
NeuralNetworkModel
.
parabolic module¶

class
pymor.reductors.parabolic.
ParabolicRBEstimator
(*args, **kwargs)[source]¶ Bases:
pymor.core.base.ImmutableObject
Instantiated by
ParabolicRBReductor
.Not to be used directly.
Methods
estimate
,estimate_error
,restricted_to_subbasis
Attributes

class
pymor.reductors.parabolic.
ParabolicRBReductor
(fom, RB=None, product=None, coercivity_estimator=None, check_orthonormality=None, check_tol=None)[source]¶ Bases:
pymor.reductors.basic.InstationaryRBReductor
Reduced Basis Reductor for parabolic equations.
This reductor uses
InstationaryRBReductor
for the actual RBprojection. The only addition is the assembly of an error estimator which bounds the discrete l2in time / energyin space error similar to [GP05], [HO08] as follows:\[\left[ C_a^{1}(\mu)\e_N(\mu)\^2 + \sum_{n=1}^{N} \Delta t\e_n(\mu)\^2_e \right]^{1/2} \leq \left[ C_a^{2}(\mu)\Delta t \sum_{n=1}^{N}\\mathcal{R}^n(u_n(\mu), \mu)\^2_{e,1} + C_a^{1}(\mu)\e_0\^2 \right]^{1/2}\]Here, \(\\cdot\\) denotes the norm induced by the problem’s mass matrix (e.g. the L^2norm) and \(\\cdot\_e\) is an arbitrary energy norm w.r.t. which the space operator \(A(\mu)\) is coercive, and \(C_a(\mu)\) is a lower bound for its coercivity constant. Finally, \(\mathcal{R}^n\) denotes the implicit Euler timestepping residual for the (fixed) time step size \(\Delta t\),
\[\mathcal{R}^n(u_n(\mu), \mu) := f  M \frac{u_{n}(\mu)  u_{n1}(\mu)}{\Delta t}  A(u_n(\mu), \mu),\]where \(M\) denotes the mass operator and \(f\) the source term. The dual norm of the residual is computed using the numerically stable projection from [BEOR14].
Parameters
 fom
The
InstationaryModel
which is to be reduced. RB
VectorArray
containing the reduced basis on which to project. product
The energy inner product
Operator
w.r.t. which the reduction error is estimated andRB
is orthonormalized. coercivity_estimator
None
or aParameterFunctional
returning a lower bound \(C_a(\mu)\) for the coercivity constant offom.operator
w.r.t.product
.
Methods
assemble_error_estimator
,assemble_error_estimator_for_subbasis
build_rom
,project_operators
,project_operators_to_subbasis
extend_basis
,reconstruct
,reduce
Attributes
residual module¶

class
pymor.reductors.residual.
ImplicitEulerResidualOperator
(*args, **kwargs)[source]¶ Bases:
pymor.operators.interface.Operator
Instantiated by
ImplicitEulerResidualReductor
.Methods
apply
,projected_to_subbasis
apply2
,apply_adjoint
,apply_inverse
,apply_inverse_adjoint
,as_range_array
,as_source_array
,as_vector
,assemble
,d_mu
,jacobian
,pairwise_apply2
,restricted
,__matmul__
Attributes
H
,linear
,range
,solver_options
,source
parameters
,parameters_inherited
,parameters_internal
,parameters_own
,parametric

apply
(U, U_old, mu=None)[source]¶ Apply the operator to a
VectorArray
.Parameters
 U
VectorArray
of vectors to which the operator is applied. mu
The
parameter values
for which to evaluate the operator.
Returns
VectorArray
of the operator evaluations.


class
pymor.reductors.residual.
ImplicitEulerResidualReductor
(RB, operator, mass, dt, rhs=None, product=None)[source]¶ Bases:
pymor.core.base.BasicObject
Reduced basis residual reductor with mass operator for implicit Euler timestepping.
Given an operator, mass and a functional, the concatenation of residual operator with the Riesz isomorphism is given by:
riesz_residual.apply(U, U_old, mu) == product.apply_inverse(operator.apply(U, mu) + 1/dt*mass.apply(U, mu)  1/dt*mass.apply(U_old, mu)  rhs.as_vector(mu))
This reductor determines a lowdimensional subspace of the image of a reduced basis space under
riesz_residual
usingestimate_image_hierarchical
, computes an orthonormal basisresidual_range
of this range space and then returns the PetrovGalerkin projectionprojected_riesz_residual == riesz_residual.projected(range_basis=residual_range, source_basis=RB)
of the
riesz_residual
operator. Given reduced basis coefficient vectorsu
andu_old
, the dual norm of the residual can then be computed asprojected_riesz_residual.apply(u, u_old, mu).norm()
Moreover, a
reconstruct
method is provided such thatresidual_reductor.reconstruct(projected_riesz_residual.apply(u, u_old, mu)) == riesz_residual.apply(RB.lincomb(u), RB.lincomb(u_old), mu)
Parameters
 operator
See definition of
riesz_residual
. mass
The mass operator. See definition of
riesz_residual
. dt
The time step size. See definition of
riesz_residual
. rhs
See definition of
riesz_residual
. IfNone
, zero righthand side is assumed. RB
VectorArray
containing a basis of the reduced space onto which to project. product
Inner product
Operator
w.r.t. which to compute the Riesz representatives.
Methods
reconstruct
,reduce
Attributes

class
pymor.reductors.residual.
NonProjectedImplicitEulerResidualOperator
(*args, **kwargs)[source]¶ Bases:
pymor.reductors.residual.ImplicitEulerResidualOperator
Instantiated by
ImplicitEulerResidualReductor
.Not to be used directly.
Methods
apply
,projected_to_subbasis
apply2
,apply_adjoint
,apply_inverse
,apply_inverse_adjoint
,as_range_array
,as_source_array
,as_vector
,assemble
,d_mu
,jacobian
,pairwise_apply2
,restricted
,__matmul__
Attributes
H
,linear
,range
,solver_options
,source
parameters
,parameters_inherited
,parameters_internal
,parameters_own
,parametric

apply
(U, U_old, mu=None)[source]¶ Apply the operator to a
VectorArray
.Parameters
 U
VectorArray
of vectors to which the operator is applied. mu
The
parameter values
for which to evaluate the operator.
Returns
VectorArray
of the operator evaluations.


class
pymor.reductors.residual.
NonProjectedResidualOperator
(*args, **kwargs)[source]¶ Bases:
pymor.reductors.residual.ResidualOperator
Instantiated by
ResidualReductor
.Not to be used directly.
Methods
apply
,projected_to_subbasis
apply2
,apply_adjoint
,apply_inverse
,apply_inverse_adjoint
,as_range_array
,as_source_array
,as_vector
,assemble
,d_mu
,jacobian
,pairwise_apply2
,restricted
,__matmul__
Attributes
H
,linear
,range
,solver_options
,source
parameters
,parameters_inherited
,parameters_internal
,parameters_own
,parametric

apply
(U, mu=None)[source]¶ Apply the operator to a
VectorArray
.Parameters
 U
VectorArray
of vectors to which the operator is applied. mu
The
parameter values
for which to evaluate the operator.
Returns
VectorArray
of the operator evaluations.


class
pymor.reductors.residual.
ResidualOperator
(*args, **kwargs)[source]¶ Bases:
pymor.operators.interface.Operator
Instantiated by
ResidualReductor
.Methods
apply
,projected_to_subbasis
apply2
,apply_adjoint
,apply_inverse
,apply_inverse_adjoint
,as_range_array
,as_source_array
,as_vector
,assemble
,d_mu
,jacobian
,pairwise_apply2
,restricted
,__matmul__
Attributes
H
,linear
,range
,solver_options
,source
parameters
,parameters_inherited
,parameters_internal
,parameters_own
,parametric

apply
(U, mu=None)[source]¶ Apply the operator to a
VectorArray
.Parameters
 U
VectorArray
of vectors to which the operator is applied. mu
The
parameter values
for which to evaluate the operator.
Returns
VectorArray
of the operator evaluations.


class
pymor.reductors.residual.
ResidualReductor
(RB, operator, rhs=None, product=None, riesz_representatives=False)[source]¶ Bases:
pymor.core.base.BasicObject
Generic reduced basis residual reductor.
Given an operator and a righthand side, the residual is given by:
residual.apply(U, mu) == operator.apply(U, mu)  rhs.as_range_array(mu)
When operator maps to functionals instead of vectors, we are interested in the Riesz representative of the residual:
residual.apply(U, mu) == product.apply_inverse(operator.apply(U, mu)  rhs.as_range_array(mu))
Given a basis
RB
of a subspace of the source space ofoperator
, this reductor usesestimate_image_hierarchical
to determine a lowdimensional subspace containing the image of the subspace underresidual
(resp.riesz_residual
), computes an orthonormal basisresidual_range
for this range space and then returns the PetrovGalerkin projectionprojected_residual == project(residual, range_basis=residual_range, source_basis=RB)
of the residual operator. Given a reduced basis coefficient vector
u
, w.r.t.RB
, the (dual) norm of the residual can then be computed asprojected_residual.apply(u, mu).norm()
Moreover, a
reconstruct
method is provided such thatresidual_reductor.reconstruct(projected_residual.apply(u, mu)) == residual.apply(RB.lincomb(u), mu)
Parameters
 RB
VectorArray
containing a basis of the reduced space onto which to project. operator
See definition of
residual
. rhs
See definition of
residual
. IfNone
, zero righthand side is assumed. product
Inner product
Operator
w.r.t. which to orthonormalize and w.r.t. which to compute the Riesz representatives in caseoperator
maps to functionals. riesz_representatives
If
True
compute the Riesz representative of the residual.
Methods
reconstruct
,reduce
Attributes
sobt module¶

class
pymor.reductors.sobt.
GenericSOBTpvReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Generic SecondOrder Balanced Truncation position/velocity reductor.
See [RS08].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

_projection_matrices_and_singular_values
(r, gramians)[source]¶ Return projection matrices and singular values.

reduce
(r, projection='bfsr')[source]¶ Reduce using GenericSOBTpv.
Parameters
 r
Order of the reduced model.
 projection
Projection method used:
'sr'
: square root method'bfsr'
: balancingfree square root method (default, since it avoids scaling by singular values and orthogonalizes the projection matrices, which might make it more accurate than the square root method)'biorth'
: like the balancingfree square root method, except it biorthogonalizes the projection matrices
Returns
 rom
Reducedorder
SecondOrderModel
.

class
pymor.reductors.sobt.
SOBTReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
SecondOrder Balanced Truncation reductor.
See [CLVV06].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

reduce
(r, projection='bfsr')[source]¶ Reduce using SOBT.
Parameters
 r
Order of the reduced model.
 projection
Projection method used:
'sr'
: square root method'bfsr'
: balancingfree square root method (default, since it avoids scaling by singular values and orthogonalizes the projection matrices, which might make it more accurate than the square root method)'biorth'
: like the balancingfree square root method, except it biorthogonalizes the projection matrices
Returns
 rom
Reducedorder
SecondOrderModel
.

class
pymor.reductors.sobt.
SOBTfvReductor
(fom, mu=None)[source]¶ Bases:
pymor.core.base.BasicObject
Freevelocity SecondOrder Balanced Truncation reductor.
See [MS96].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

reduce
(r, projection='bfsr')[source]¶ Reduce using SOBTfv.
Parameters
 r
Order of the reduced model.
 projection
Projection method used:
'sr'
: square root method'bfsr'
: balancingfree square root method (default, since it avoids scaling by singular values and orthogonalizes the projection matrices, which might make it more accurate than the square root method)'biorth'
: like the balancingfree square root method, except it biorthogonalizes the projection matrices
Returns
 rom
Reducedorder
SecondOrderModel
.

class
pymor.reductors.sobt.
SOBTpReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.sobt.GenericSOBTpvReductor
SecondOrder Balanced Truncation position reductor.
See [RS08].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

class
pymor.reductors.sobt.
SOBTpvReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.sobt.GenericSOBTpvReductor
SecondOrder Balanced Truncation positionvelocity reductor.
See [RS08].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

class
pymor.reductors.sobt.
SOBTvReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.sobt.GenericSOBTpvReductor
SecondOrder Balanced Truncation velocity reductor.
See [RS08].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes

class
pymor.reductors.sobt.
SOBTvpReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.sobt.GenericSOBTpvReductor
SecondOrder Balanced Truncation velocityposition reductor.
See [RS08].
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Attributes
sor_irka module¶
IRKAtype reductor for SecondOrderModels
.

class
pymor.reductors.sor_irka.
SORIRKAReductor
(fom, mu=None)[source]¶ Bases:
pymor.reductors.h2.GenericIRKAReductor
SORIRKA reductor.
Parameters
 fom
The fullorder
SecondOrderModel
to reduce. mu
Methods
Attributes

reduce
(rom0_params, tol=0.0001, maxit=100, num_prev=1, force_sigma_in_rhp=False, projection='orth', conv_crit='sigma', compute_errors=False, irka_options=None)[source]¶ Reduce using SORIRKA.
It uses IRKA as the intermediate reductor, to reduce from 2r to r poles. See Section 5.3.2 in [W12].
Parameters
 rom0_params
Can be:
order of the reduced model (a positive integer),
dict with
'sigma'
,'b'
,'c'
as keys mapping to initial interpolation points (a 1DNumPy array
), right tangential directions (VectorArray
fromfom.D.source
), and left tangential directions (VectorArray
fromfom.D.range
), all of the same length (the order of the reduced model),initial reducedorder model (
LTIModel
).
If the order of reduced model is given, initial interpolation data is generated randomly.
 tol
Tolerance for the convergence criterion.
 maxit
Maximum number of iterations.
 num_prev
Number of previous iterations to compare the current iteration to. Larger number can avoid occasional cyclic behavior of IRKA.
 force_sigma_in_rhp
If
False
, new interpolation are reflections of the current reduced order model’s poles. Otherwise, only the poles in the left halfplane are reflected. projection
Projection method:
'orth'
: projection matrices are orthogonalized with respect to the Euclidean inner product'biorth'
: projection matrices are biorthogolized with respect to the E product
 conv_crit
Convergence criterion:
'sigma'
: relative change in interpolation points'h2'
: relative \(\mathcal{H}_2\) distance of reducedorder models
 compute_errors
Should the relative \(\mathcal{H}_2\)errors of intermediate reduced order models be computed.
Warning
Computing \(\mathcal{H}_2\)errors is expensive. Use this option only if necessary.
 irka_options
Dict of options for IRKAReductor.reduce.
Returns
 rom
Reducedorder
SecondOrderModel
.