pymoto.AssembleStiffness

class pymoto.AssembleStiffness(domain: VoxelDomain, *args, e_modulus: float = 1.0, poisson_ratio: float = 0.3, plane='strain', **kwargs)

Stiffness matrix assembly by scaling elements in 2D or 3D \(\mathbf{K} = \sum_e x_e \mathbf{K}_e\)

Input Signal:

  • x: Scaling vector of size (Nel)

Output Signal:

  • K: Stiffness matrix of size (n, n)

__init__(domain: VoxelDomain, *args, e_modulus: float = 1.0, poisson_ratio: float = 0.3, plane='strain', **kwargs)

Initialize stiffness assembly module

Parameters:

  • domain (pymoto.VoxelDomain) – The domain to assemble for; this determines the element size and dimensionality

  • *args – Other arguments are passed to pymoto.AssembleGeneral

  • e_modulus (float, optional) – Young’s modulus. Defaults to 1.0.

  • poisson_ratio (float, optional) – Poisson’s ratio. Defaults to 0.3.

  • plane (str, optional) – Plane “strain” or plane “stress”. Defaults to “strain”.

  • **kwargs – Other keyword arguments are passed to pymoto.AssembleGeneral

Methods

__init__(domain, *args[, e_modulus, ...])

Initialize stiffness assembly module

connect(sig_in[, sig_out])

Connect without automatic adding to a function network

get_input_sensitivities([as_list])

get_input_states([as_list])

get_output_sensitivities([as_list])

get_output_states([as_list])

reset()

Reset the state of the sensitivities (they are set to zero or to None)

response()

Calculate the response from sig_in and output this to sig_out

sensitivity()

Calculate sensitivities using backpropagation

Attributes

n_in

Get the number of input signals

n_out

Get the number of output signals

sig_in

sig_out

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None)

Connect without automatic adding to a function network

get_input_sensitivities(as_list=False)
get_input_states(as_list=False)
get_output_sensitivities(as_list=False)
get_output_states(as_list=False)
property n_in: int

Get the number of input signals

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset()

Reset the state of the sensitivities (they are set to zero or to None)

response()

Calculate the response from sig_in and output this to sig_out

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in

sig_in: List = None
sig_out: List = None