def obj_fun(self, state_dp):
"""
Objective function evaluation for given direct problem state.
"""
var_data = state_dp.get_parts()
var_data = remap_dict(var_data, self.var_map)
self.of_equations.set_data(var_data, ignore_unknown=True)
val = eval_equations(self.of_equations, self.of_variables)
return nm.squeeze( val )
def solve_adjoint(conf, options, dpb, vec_dp, data):
"""
Solve the adjoint (linear) problem.
"""
opts = conf.options
if dpb:
apb = dpb.copy('adjoint')
else:
apb = ProblemDefinition.from_conf(conf)
equations = getattr(conf, '_'.join(('equations_adjoint',
opts.problem,
opts.objective_function)))
apb.set_equations(equations)
apb.time_update(None)
apb.ebcs.zero_dofs()
apb.update_equations(None, ebcs=apb.ebcs)
var_data = dpb.equations.get_state_parts(vec_dp)
var_data = remap_dict(var_data, opts.var_map)
nls_conf = apb.get_solver_conf(opts.nls_adjoint)
vec_ap = apb.solve(nls_conf=nls_conf, var_data=var_data)
trunk = io.get_trunk(conf.filename_mesh)
apb.save_state(trunk + '_adjoint.vtk', vec_ap)
shape_opt = so.ShapeOptimFlowCase.from_conf(conf, dpb, apb)
## print shape_opt
## pause()
if options.test is not None:
##
# Test shape sensitivity.
if shape_opt.test_terms_if_test:
so.test_terms([options.test], opts.term_delta, shape_opt,
var_data, vec_ap)
shape_opt.check_sensitivity([options.test], opts.delta,
var_data, vec_ap)
##
# Compute objective function.
val = shape_opt.obj_fun(vec_dp)
print 'actual obj_fun:', val
## pause()
##
# Compute shape sensitivity.
vec_sa = shape_opt.sensitivity(var_data, vec_ap)
print 'actual sensitivity:', vec_sa
def solve_adjoint(conf, options, dpb, state_dp, data):
"""
Solve the adjoint (linear) problem.
"""
opts = conf.options
if dpb:
apb = dpb.copy('adjoint')
else:
apb = Problem.from_conf(conf, init_equations=False)
equations = getattr(
conf, '_'.join(
('equations_adjoint', opts.problem, opts.objective_function)))
apb.set_equations(equations)
apb.time_update(None)
apb.ebcs.zero_dofs()
apb.update_equations(None, ebcs=apb.ebcs)
var_data = state_dp.get_parts()
var_data = remap_dict(var_data, opts.var_map)
nls_conf = apb.get_solver_conf(opts.nls_adjoint)
state_ap = apb.solve(nls_conf=nls_conf, var_data=var_data)
trunk = io.get_trunk(conf.filename_mesh)
apb.save_state(trunk + '_adjoint.vtk', state_ap)
shape_opt = so.ShapeOptimFlowCase.from_conf(conf, dpb, apb)
if options.test is not None:
##
# Test shape sensitivity.
if shape_opt.test_terms_if_test:
so.test_terms([options.test], opts.term_delta, shape_opt, var_data,
state_ap)
shape_opt.check_sensitivity([options.test], opts.delta, var_data,
state_ap)
##
# Compute objective function.
val = shape_opt.obj_fun(state_dp)
print 'actual obj_fun:', val
##
# Compute shape sensitivity.
vec_sa = shape_opt.sensitivity(var_data, state_ap)
print 'actual sensitivity:', vec_sa
def obj_fun_grad(design, shape_opt, opts):
"""
The objective function gradient evaluation.
"""
state_dp = solve_problem_for_design(shape_opt.dpb, design, shape_opt, opts)
var_data = state_dp.get_parts()
var_data = remap_dict(var_data, shape_opt.var_map)
state_ap = solve_problem_for_design(shape_opt.apb, design, shape_opt, opts,
var_data=var_data,
use_cache=False, is_mesh_update=False)
vec_sa = shape_opt.sensitivity(var_data, state_ap)
return vec_sa
