pymoto.AssemblePoisson

class pymoto.AssemblePoisson(domain: VoxelDomain, *args, material_property: float = 1.0, **kwargs)

Assembly of matrix to solve Poisson equation (e.g. Thermal conductivity, Electric permittivity) \(\mathbf{P} = \sum_e x_e \mathbf{P}_e\)

Input Signal:

Output Signal:

__init__(domain: VoxelDomain, *args, material_property: float = 1.0, **kwargs)

Initialize Poisson matrix 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
material_property (float, optional) – Material property (e.g. thermal conductivity, electric permittivity). Defaults to 1.0.
**kwargs – Other keyword arguments are passed to pymoto.AssembleGeneral

Methods

`__init__`(domain, *args[, material_property])	Initialize Poisson matrix 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

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