pymor.discretizers.builtin.fv

This module provides some operators for finite volume discretizations.

Module Contents

class pymor.discretizers.builtin.fv.BoundaryL2Functional(grid, function, boundary_type=None, boundary_info=None, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

FV functional representing the inner product with an L2-Function on the boundary.

Parameters

grid

Grid for which to assemble the functional.

function

The Function with which to take the inner product.

boundary_type

The type of domain boundary (e.g. ‘neumann’) on which to assemble the functional. If None the functional is assembled over the whole boundary.

boundary_info

If boundary_type is specified, the BoundaryInfo determining which boundary entity belongs to which physical boundary.

name

The name of the functional.

source[source]
sparse = False[source]
class pymor.discretizers.builtin.fv.DiffusionOperator(grid, boundary_info, diffusion_function=None, diffusion_constant=None, solver_options=None, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Finite Volume Diffusion Operator.

The operator is of the form

(Lu)(x) = c ∇ ⋅ [ d(x) ∇ u(x) ]

Parameters

grid

The Grid over which to assemble the operator.

boundary_info

BoundaryInfo for the treatment of Dirichlet boundary conditions.

diffusion_function

The scalar-valued Function d(x). If None, constant one is assumed.

diffusion_constant

The constant c. If None, c is set to one.

solver_options

The solver_options for the operator.

name

Name of the operator.

sparse = True[source]
class pymor.discretizers.builtin.fv.EngquistOsherFlux(flux, flux_derivative, gausspoints=5, intervals=1)[source]

Bases: NumericalConvectiveFlux

Engquist-Osher numerical flux.

If f is the analytical flux, and f' its derivative, the Engquist-Osher flux is given by:

F(U_in, U_out, normal, vol) = vol * [c^+(U_in, normal)  +  c^-(U_out, normal)]

                                   U_in
c^+(U_in, normal)  = f(0)⋅normal +  ∫   max(f'(s)⋅normal, 0) ds
                                   s=0

                                  U_out
c^-(U_out, normal) =                ∫   min(f'(s)⋅normal, 0) ds
                                   s=0

Parameters

flux

Function defining the analytical flux f.

flux_derivative

Function defining the analytical flux derivative f'.

gausspoints

Number of Gauss quadrature points to be used for integration.

intervals

Number of subintervals to be used for integration.

evaluate_stage1(self, U, mu=None)[source]
evaluate_stage2(self, stage1_data, unit_outer_normals, volumes, mu=None)[source]
class pymor.discretizers.builtin.fv.InterpolationOperator(grid, function, order=0)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Vector-like L^2-projection interpolation Operator for finite volume spaces.

Parameters

grid

The Grid on which to interpolate.

function

The Function to interpolate.

order

The quadrature order to compute the element-wise averages

linear = True[source]
source[source]
class pymor.discretizers.builtin.fv.L2Functional(grid, function=None, boundary_info=None, dirichlet_data=None, diffusion_function=None, diffusion_constant=None, neumann_data=None, order=1, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Finite volume functional representing the inner product with an L2-Function.

Additionally, boundary conditions can be enforced by providing dirichlet_data and neumann_data functions.

Parameters

grid

Grid for which to assemble the functional.

function

The Function with which to take the inner product or None.

boundary_info

BoundaryInfo determining the Dirichlet and Neumann boundaries or None. If None, no boundary treatment is performed.

dirichlet_data

Function providing the Dirichlet boundary values. If None, constant-zero boundary is assumed.

diffusion_function

See DiffusionOperator. Has to be specified in case dirichlet_data is given.

diffusion_constant

See DiffusionOperator. Has to be specified in case dirichlet_data is given.

neumann_data

Function providing the Neumann boundary values. If None, constant-zero is assumed.

order

Order of the Gauss quadrature to use for numerical integration.

name

The name of the functional.

source[source]
sparse = False[source]
class pymor.discretizers.builtin.fv.L2Product(grid, solver_options=None, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Operator representing the L2-product between finite volume functions.

Parameters

grid

The Grid for which to assemble the product.

solver_options

The solver_options for the operator.

name

The name of the product.

sparse = True[source]
class pymor.discretizers.builtin.fv.LaxFriedrichsFlux(flux, lxf_lambda=1.0)[source]

Bases: NumericalConvectiveFlux

Lax-Friedrichs numerical flux.

If f is the analytical flux, the Lax-Friedrichs flux F is given by:

F(U_in, U_out, normal, vol) = vol * [normal⋅(f(U_in) + f(U_out))/2 + (U_in - U_out)/(2*λ)]

Parameters

flux

Function defining the analytical flux f.

lxf_lambda

The stabilization parameter λ.

evaluate_stage1(self, U, mu=None)[source]
evaluate_stage2(self, stage1_data, unit_outer_normals, volumes, mu=None)[source]
class pymor.discretizers.builtin.fv.LinearAdvectionLaxFriedrichsOperator(grid, boundary_info, velocity_field, lxf_lambda=1.0, solver_options=None, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Linear advection finite Volume Operator using Lax-Friedrichs flux.

The operator is of the form

L(u, mu)(x) = ∇ ⋅ (v(x, mu)⋅u(x))

See LaxFriedrichsFlux for the definition of the Lax-Friedrichs flux.

Parameters

grid

Grid over which to assemble the operator.

boundary_info

BoundaryInfo determining the Dirichlet and Neumann boundaries.

velocity_field

Function defining the velocity field v.

lxf_lambda

The stabilization parameter λ.

solver_options

The solver_options for the operator.

name

The name of the operator.

class pymor.discretizers.builtin.fv.NonlinearAdvectionOperator(grid, boundary_info, numerical_flux, dirichlet_data=None, solver_options=None, space_id='STATE', name=None)[source]

Bases: pymor.operators.interface.Operator

Nonlinear finite volume advection Operator.

The operator is of the form

L(u, mu)(x) = ∇ ⋅ f(u(x), mu)

Parameters

grid

Grid for which to evaluate the operator.

boundary_info

BoundaryInfo determining the Dirichlet and Neumann boundaries.

numerical_flux

The NumericalConvectiveFlux to use.

dirichlet_data

Function providing the Dirichlet boundary values. If None, constant-zero boundary is assumed.

solver_options

The solver_options for the operator.

name

The name of the operator.

Methods

apply

Apply the operator to a VectorArray.

jacobian

Return the operator's Jacobian as a new Operator.

restricted

Restrict the operator range to a given set of degrees of freedom.

with_numerical_flux

linear = False[source]
apply(self, 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.

jacobian(self, U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The parameter values for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

restricted(self, dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
    == restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))

Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

with_numerical_flux(self, **kwargs)[source]
class pymor.discretizers.builtin.fv.NonlinearReactionOperator(grid, reaction_function, reaction_function_derivative=None, space_id='STATE', name=None)[source]

Bases: pymor.operators.interface.Operator

Interface for Parameter dependent discrete operators.

An operator in pyMOR is simply a mapping which for any given parameter values maps vectors from its source VectorSpace to vectors in its range VectorSpace.

Note that there is no special distinction between functionals and operators in pyMOR. A functional is simply an operator with NumpyVectorSpace (1) as its range VectorSpace.

solver_options[source]

If not None, a dict which can contain the following keys:

‘inverse’:

solver options used for apply_inverse

‘inverse_adjoint’:

solver options used for apply_inverse_adjoint

‘jacobian’:

solver options for the operators returned by jacobian (has no effect for linear operators)

If solver_options is None or a dict entry is missing or None, default options are used. The interpretation of the given solver options is up to the operator at hand. In general, values in solver_options should either be strings (indicating a solver type) or dicts of options, usually with an entry 'type' which specifies the solver type to use and further items which configure this solver.

linear[source]

True if the operator is linear.

source[source]

The source VectorSpace.

range[source]

The range VectorSpace.

H[source]

The adjoint operator, i.e.

self.H.apply(V, mu) == self.apply_adjoint(V, mu)

for all V, mu.

Methods

apply

Apply the operator to a VectorArray.

jacobian

Return the operator's Jacobian as a new Operator.

apply(self, U, ind=None, 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.

jacobian(self, U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The parameter values for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

class pymor.discretizers.builtin.fv.NumericalConvectiveFlux[source]

Bases: pymor.parameters.base.ParametricObject

Interface for numerical convective fluxes for finite volume schemes.

Numerical fluxes defined by this interfaces are functions of the form F(U_inner, U_outer, unit_outer_normal, edge_volume, mu).

The flux evaluation is vectorized and happens in two stages:
  1. evaluate_stage1 receives a NumPy array U of all values which appear as U_inner or U_outer for all edges the flux shall be evaluated at and returns a tuple of NumPy arrays each of the same length as U.

  2. evaluate_stage2 receives the reordered stage1_data for each edge as well as the unit outer normal and the volume of the edges.

    stage1_data is given as follows: If R_l is l-th entry of the tuple returned by evaluate_stage1, the l-th entry D_l of of the stage1_data tuple has the shape (num_edges, 2) + R_l.shape[1:]. If for edge k the values U_inner and U_outer are the i-th and j-th value in the U array provided to evaluate_stage1, we have

    D_l[k, 0] == R_l[i],    D_l[k, 1] == R_l[j].
    

    evaluate_stage2 returns a NumPy array of the flux evaluations for each edge.

abstract evaluate_stage1(self, U, mu=None)[source]
abstract evaluate_stage2(self, stage1_data, unit_outer_normals, volumes, mu=None)[source]
class pymor.discretizers.builtin.fv.ReactionOperator(grid, reaction_coefficient, solver_options=None, name=None)[source]

Bases: pymor.operators.numpy.NumpyMatrixBasedOperator

Finite Volume reaction Operator.

The operator is of the form

L(u, mu)(x) = c(x, mu)⋅u(x)

Parameters

grid

The Grid for which to assemble the operator.

reaction_coefficient

The function ‘c’

solver_options

The solver_options for the operator.

name

The name of the operator.

sparse = True[source]
class pymor.discretizers.builtin.fv.SimplifiedEngquistOsherFlux(flux, flux_derivative)[source]

Bases: NumericalConvectiveFlux

Engquist-Osher numerical flux. Simplified Implementation for special case.

For the definition of the Engquist-Osher flux see EngquistOsherFlux. This class provides a faster and more accurate implementation for the special case that f(0) == 0 and the derivative of f only changes sign at 0.

Parameters

flux

Function defining the analytical flux f.

flux_derivative

Function defining the analytical flux derivative f'.

evaluate_stage1(self, U, mu=None)[source]
evaluate_stage2(self, stage1_data, unit_outer_normals, volumes, mu=None)[source]
pymor.discretizers.builtin.fv.FVVectorSpace(grid, id='STATE')[source]
pymor.discretizers.builtin.fv.discretize_instationary_fv(analytical_problem, diameter=None, domain_discretizer=None, grid_type=None, num_flux='lax_friedrichs', lxf_lambda=1.0, eo_gausspoints=5, eo_intervals=1, grid=None, boundary_info=None, num_values=None, time_stepper=None, nt=None, preassemble=True)[source]

FV Discretization of an InstationaryProblem with a StationaryProblem as stationary part

Parameters

analytical_problem

The InstationaryProblem to discretize.

diameter

If not None, diameter is passed to the domain_discretizer.

domain_discretizer

Discretizer to be used for discretizing the analytical domain. This has to be a function domain_discretizer(domain_description, diameter, ...). If further arguments should be passed to the discretizer, use functools.partial. If None, discretize_domain_default is used.

grid_type

If not None, this parameter is forwarded to domain_discretizer to specify the type of the generated Grid.

num_flux

The numerical flux to use in the finite volume formulation. Allowed values are 'lax_friedrichs', 'engquist_osher', 'simplified_engquist_osher' (see pymor.discretizers.builtin.fv).

lxf_lambda

The stabilization parameter for the Lax-Friedrichs numerical flux (ignored, if different flux is chosen).

eo_gausspoints

Number of Gauss points for the Engquist-Osher numerical flux (ignored, if different flux is chosen).

eo_intervals

Number of sub-intervals to use for integration when using Engquist-Osher numerical flux (ignored, if different flux is chosen).

grid

Instead of using a domain discretizer, the Grid can also be passed directly using this parameter.

boundary_info

A BoundaryInfo specifying the boundary types of the grid boundary entities. Must be provided if grid is specified.

num_values

The number of returned vectors of the solution trajectory. If None, each intermediate vector that is calculated is returned.

time_stepper

The time-stepper to be used by solve.

nt

If time_stepper is not specified, the number of time steps for implicit Euler time stepping.

preassemble

If True, preassemble all operators in the resulting Model.

Returns

m

The Model that has been generated.

data

Dictionary with the following entries:

grid:

The generated Grid.

boundary_info:

The generated BoundaryInfo.

unassembled_m:

In case preassemble is True, the generated Model before preassembling operators.

pymor.discretizers.builtin.fv.discretize_stationary_fv(analytical_problem, diameter=None, domain_discretizer=None, grid_type=None, num_flux='lax_friedrichs', lxf_lambda=1.0, eo_gausspoints=5, eo_intervals=1, grid=None, boundary_info=None, preassemble=True)[source]

Discretizes a StationaryProblem using the finite volume method.

Parameters

analytical_problem

The StationaryProblem to discretize.

diameter

If not None, diameter is passed as an argument to the domain_discretizer.

domain_discretizer

Discretizer to be used for discretizing the analytical domain. This has to be a function domain_discretizer(domain_description, diameter, ...). If None, discretize_domain_default is used.

grid_type

If not None, this parameter is forwarded to domain_discretizer to specify the type of the generated Grid.

num_flux

The numerical flux to use in the finite volume formulation. Allowed values are 'lax_friedrichs', 'engquist_osher', 'simplified_engquist_osher' (see pymor.discretizers.builtin.fv).

lxf_lambda

The stabilization parameter for the Lax-Friedrichs numerical flux (ignored, if different flux is chosen).

eo_gausspoints

Number of Gauss points for the Engquist-Osher numerical flux (ignored, if different flux is chosen).

eo_intervals

Number of sub-intervals to use for integration when using Engquist-Osher numerical flux (ignored, if different flux is chosen).

grid

Instead of using a domain discretizer, the Grid can also be passed directly using this parameter.

boundary_info

A BoundaryInfo specifying the boundary types of the grid boundary entities. Must be provided if grid is specified.

preassemble

If True, preassemble all operators in the resulting Model.

Returns

m

The Model that has been generated.

data

Dictionary with the following entries:

grid:

The generated Grid.

boundary_info:

The generated BoundaryInfo.

unassembled_m:

In case preassemble is True, the generated Model before preassembling operators.

pymor.discretizers.builtin.fv.jacobian_options(delta=1e-07)[source]
pymor.discretizers.builtin.fv.nonlinear_advection_engquist_osher_operator(grid, boundary_info, flux, flux_derivative, gausspoints=5, intervals=1, dirichlet_data=None, solver_options=None, name=None)[source]

Instantiate a NonlinearAdvectionOperator using EngquistOsherFlux.

pymor.discretizers.builtin.fv.nonlinear_advection_lax_friedrichs_operator(grid, boundary_info, flux, lxf_lambda=1.0, dirichlet_data=None, solver_options=None, name=None)[source]

Instantiate a NonlinearAdvectionOperator using LaxFriedrichsFlux.

pymor.discretizers.builtin.fv.nonlinear_advection_simplified_engquist_osher_operator(grid, boundary_info, flux, flux_derivative, dirichlet_data=None, solver_options=None, name=None)[source]

Create a NonlinearAdvectionOperator using SimplifiedEngquistOsherFlux.