Source code for pymor.analyticalproblems.elliptic

# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2019 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)

import numpy as np

from pymor.core.interfaces import ImmutableInterface
from pymor.tools.frozendict import FrozenDict


[docs]class StationaryProblem(ImmutableInterface): """Linear elliptic problem description. The problem consists in solving :: - ∇ ⋅ [d(x, μ) ∇ u(x, μ)] + ∇ ⋅ [f(x, u(x, μ), μ)] + c(x, u(x, μ), μ) = f(x, μ) for u. Parameters ---------- domain A |DomainDescription| of the domain the problem is posed on. rhs The |Function| f(x, μ). `rhs.dim_domain` has to agree with the dimension of `domain`, whereas `rhs.shape_range` has to be `()`. diffusion The |Function| d(x, μ) with `shape_range` of either `()` or `(dim domain, dim domain)`. advection The |Function| f, only depending on x, with `shape_range` of `(dim domain,)`. nonlinear_advection The |Function| f, only depending on u, with `shape_range` of `(dim domain,)`. nonlinear_advection_derivative The derivative of f, only depending on u, with respect to u. reaction The |Function| c, only depending on x, with `shape_range` of `()`. nonlinear_reaction The |Function| c, only depending on u, with `shape_range` of `()`. nonlinear_reaction_derivative The derivative of the |Function| c, only depending on u, with `shape_range` of `()`. dirichlet_data |Function| providing the Dirichlet boundary values. neumann_data |Function| providing the Neumann boundary values. robin_data Tuple of two |Functions| providing the Robin parameter and boundary values. outputs Tuple of additional output functionals to assemble. Each value must be a tuple of the form `(functional_type, data)` where `functional_type` is a string defining the type of functional to assemble and `data` is a |Function| holding the corresponding coefficient function. Currently implemented `functional_types` are: :l2: Evaluate the l2-product with the given data function. :l2_boundary: Evaluate the l2-product with the given data function on the boundary. parameter_space |ParameterSpace| for the problem. name Name of the problem. Attributes ---------- domain rhs diffusion advection nonlinear_advection nonlinear_advection_derivative reaction nonlinear_reaction nonlinear_reaction_derivative dirichlet_data neumann_data robin_data outputs """ def __init__(self, domain, rhs=None, diffusion=None, advection=None, nonlinear_advection=None, nonlinear_advection_derivative=None, reaction=None, nonlinear_reaction=None, nonlinear_reaction_derivative=None, dirichlet_data=None, neumann_data=None, robin_data=None, outputs=None, parameter_space=None, name=None): assert (rhs is None or rhs.dim_domain == domain.dim and rhs.shape_range == ()) assert (diffusion is None or diffusion.dim_domain == domain.dim and diffusion.shape_range in ((), (domain.dim, domain.dim))) assert (advection is None or advection.dim_domain == domain.dim and advection.shape_range == (domain.dim,)) assert (nonlinear_advection is None or nonlinear_advection.dim_domain == 1 and nonlinear_advection.shape_range == (domain.dim,)) assert (nonlinear_advection_derivative is None or (nonlinear_advection_derivative.dim_domain == 1 and nonlinear_advection_derivative.shape_range == (domain.dim,))) assert (reaction is None or reaction.dim_domain == domain.dim and reaction.shape_range == ()) assert (nonlinear_reaction is None or nonlinear_reaction.dim_domain == 1 and nonlinear_reaction.shape_range == ()) assert (nonlinear_reaction_derivative is None or nonlinear_reaction_derivative.dim_domain == 1 and nonlinear_reaction_derivative.shape_range == ()) assert (dirichlet_data is None or dirichlet_data.dim_domain == domain.dim and dirichlet_data.shape_range == ()) assert (neumann_data is None or neumann_data.dim_domain == domain.dim and neumann_data.shape_range == ()) assert (robin_data is None or (isinstance(robin_data, tuple) and len(robin_data) == 2 and np.all([f.dim_domain == domain.dim and f.shape_range == () for f in robin_data]))) assert (outputs is None or all(isinstance(v, tuple) and len(v) == 2 and v[0] in ('l2', 'l2_boundary') and v[1].dim_domain == domain.dim and v[1].shape_range == () for v in outputs)) outputs = tuple(outputs) if outputs is not None else None self.__auto_init(locals())