# Copyright (c) 2023, The University of Texas at Austin
# & Georgia Institute of Technology
#
# All Rights reserved.
# See file COPYRIGHT for details.
#
# This file is part of the SOUPy package. For more information see
# https://github.com/hippylib/soupy/
#
# SOUPy is free software; you can redistribute it and/or modify it under the
# terms of the GNU General Public License (as published by the Free
# Software Foundation) version 3.0 dated June 2007.
import dolfin as dl
[docs]
class NonlinearVariationalSolver:
"""
Solver for the nonlinear variational problem
.. math:: \\text{Find } u \in U \qquad r(u,v) = 0 \quad \\forall v \in V
The user must provide the variational forms for the residual form, \
an initial guess, boundary conditions, and optionally, \
variational forms for the Jacobian and backtracking cost functional.
This wraps the `dolfin.NonlinearVariationalProblem` and `dolfin.NonlinearVariationalSolver` \
classes using a different function signature
"""
def __init__(self, parameters=None):
"""
Constructor:
:param parameters: Solver parameters for the `dolfin.NonlinearVariationalSolver` class
:type parameters: dict
Class will update the default `dolfin.NonlinearVariationalSolver` parameters with the \
data in :code:`parameters` if it is provided. Otherwise, default parameters \
will be used
"""
self.parameters = parameters
[docs]
def solve(self, residual_form, u, bcs, J_form, form_compiler_parameters=None):
"""
Solve the nonlinear variational system
.. math:: \\text{Find } u \in U \qquad r(u,v) = 0 \quad \\forall v \in V
given using the `dolfin.NonlinearVariationalSolver` with a supplied initial guess
:param residual_form: Variational form for the residual
:param u: Initial guess for the solution and function used in :code:`residual_form`
:type u: :py:class:`dolfin.Function`
:param bcs: List of boundary conditions
:type bcs: list
:param J_form: Variational form for the Jacobian of the residual
:param energy_form: Optional variational form for the energy functional.
If supplied, uses this as the backtracking cost
:param form_compiler_parameters: Optional form compiler_parameters
"""
nonlinear_problem = dl.NonlinearVariationalProblem(residual_form, u, bcs, J_form,
form_compiler_parameters=form_compiler_parameters)
solver = dl.NonlinearVariationalSolver(nonlinear_problem)
if self.parameters is not None:
solver.parameters.update(self.parameters)
num_iters, converged = solver.solve()
return num_iters, converged