pymoto.NodalOperation

class pymoto.NodalOperation(domain: VoxelDomain, element_matrix: ndarray)

Generic module for nodal operations based on elemental information

\(u_e = \mathbf{A} x_e\)

This module is the reverse of pymoto.ElementOperation.

Input Signal:

Output Signal:

__init__(domain: VoxelDomain, element_matrix: ndarray)

Initialize nodal operation module

Parameters:

domain (pymoto.VoxelDomain) – The finite element domain
element_matrix (np.ndarray) – The element operator matrix \(\mathbf{A}\) of size (..., #dofs_per_element)

Methods

`__init__`(domain, element_matrix)	Initialize nodal operation 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

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)

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