TransportMaps.Distributions.Examples.Timoshenko.TimoshenkoProblems

Module Contents

Classes

TimoshenkoSolver	[Abstract] Generic class for a PDE solver
AdjointTimoshenkoSolver	\[\]
ClampedTimoshenkoProblem	\[\]

class TransportMaps.Distributions.Examples.Timoshenko.TimoshenkoProblems.TimoshenkoSolver(**kwargs)

Bases: TransportMaps.DOLFIN.Solver

[Abstract] Generic class for a PDE solver

It offers only stub functions and a function to convert degrees of freedom to fenics functions defined on the prescribed approximation space.

set_up(**kwargs)

new_function(FS)

project(f, FS)

dof_to_fun(x)

solve(E)

Parameters:

E (ndarray) – spatially varying Young’s modulus (GPa)

class TransportMaps.Distributions.Examples.Timoshenko.TimoshenkoProblems.AdjointTimoshenkoSolver(*args, **kwargs)

Bases: TimoshenkoSolver

\[J(u) = \sum_{i=1}^{n_\text{obs}} w_i \left( y_i - \int u s_i dx \right)^2\]

property sensors_list

property obs_list

property weights_list

_serializable = ['_sens_pos_list', '_sens_geo_eps', '_obs_list', '_weights_list']

set_up(**kwargs)

set_sensors_list(sens_pos_list, sens_geo_eps)

set_data(obs_list, sens_pos_list, sens_geo_eps, weights_list=None)

__getstate__()

__setstate__(state)

new_function(FS)

project(f, FS)

_set_up_reduced_functional()

E is in GPa

observe(E)

Parameters:

E (ndarray) – spatially varying Young’s modulus (GPa)

Returns:

observations though the sensors

evaluate(E)

Parameters:

E (ndarray) – spatially varying Young’s modulus (GPa)

grad_x(E)

Parameters:

E (ndarray) – spatially varying Young’s modulus (GPa)

tuple_grad_x(E)

Parameters:

E (ndarray) – spatially varying Young’s modulus (GPa)

class TransportMaps.Distributions.Examples.Timoshenko.TimoshenkoProblems.ClampedTimoshenkoProblem(**kwargs)

Bases: AdjointTimoshenkoSolver

\[J(u) = \sum_{i=1}^{n_\text{obs}} w_i \left( y_i - \int u s_i dx \right)^2\]

_init_args = ['q', 'thickness', 'width', 'length', 'kappa', 'poisson', 'nels']

__getstate__()

set_up()