Model Class Reference

Detailed Description

The canonical type for constitutive models in NEML2.

Public Member Functions
TensorValue	__getattr__ (self, str arg0)
None	__setattr__ (self, str arg0, neml2.TensorLike arg1)
str	__str__ (self)
dict[str, dict[str, neml2.Tensor]]	dvalue (self, dict arg0)
TensorValue	get_parameter (self, str arg0)
neml2.tensors.TensorType	input_type (self, str variable)
list[str]	input_variables (self)
dict[str, TensorValue]	named_buffers (self)
dict[str, TensorValue]	named_parameters (self)
dict[str, Model]	named_submodels (self)
neml2.tensors.TensorType	output_type (self, str variable)
list[str]	output_variables (self)
None	set_output_derivative_filter (self, list[tuple[str, str]] derivs)
None	set_parameter (self, str arg0, neml2.TensorLike arg1)
None	set_parameters (self, dict[str, neml2.TensorLike] arg0)
None	to (self, *, torch.dtype dtype=..., torch.device device=..., bool requires_grad=False)
dict[str, neml2.Tensor]	value (self, dict arg0)
tuple[dict[str, neml2.Tensor], dict[str, dict[str, neml2.Tensor]]]	value_and_dvalue (self, dict arg0)
None	zero_undefined_input (self)
str	type (self)

Member Function Documentation

__getattr__()

TensorValue __getattr__	(		self,
		str	arg0 )

Get a model parameter given its name

__setattr__()

None __setattr__	(		self,
		str	arg0,
		neml2.TensorLike	arg1 )

Set the value for a model parameter

__str__()

str __str__

(

self

)

dvalue()

dict[str, dict[str, neml2.Tensor]] dvalue	(		self,
		dict	arg0 )

get_parameter()

TensorValue get_parameter	(		self,
		str	arg0 )

Get a model parameter given its name

input_type()

neml2.tensors.TensorType input_type	(		self,
		str	variable )

Introspect the underlying tensor type of an input variable. @returns tensors.TensorType

input_variables()

list[str] input_variables

(

self

)

Input variables of the model.

named_buffers()

dict[str, TensorValue] named_buffers

(

self

)

Get the model buffers. The keys of the returned dictionary are the buffers' names.

named_parameters()

dict[str, TensorValue] named_parameters

(

self

)

Get the model parameters. The keys of the returned dictionary are the parameters' names.

named_submodels()

dict[str, Model] named_submodels

(

self

)

Get the sub-models registered to this model

output_type()

neml2.tensors.TensorType output_type	(		self,
		str	variable )

Introspect the underlying tensor type of an output variable. @returns tensors.TensorType

output_variables()

list[str] output_variables

(

self

)

Output variables of the model.

set_output_derivative_filter()

None set_output_derivative_filter	(		self,
		list[tuple[str, str]]	derivs )

Filter which (output, input) derivative pairs are computed and returned by dvalue and value_and_dvalue. Pass an empty list to clear the filter and compute all derivatives.

set_parameter()

None set_parameter	(		self,
		str	arg0,
		neml2.TensorLike	arg1 )

Set the value for a model parameter

set_parameters()

None set_parameters	(		self,
		dict[str, neml2.TensorLike]	arg0 )

Set the values for multiple model parameters 

to()

None to	(		self,
		*	,
		torch.dtype	dtype = ...,
		torch.device	device = ...,
		bool	requires_grad = False )

type()

str type

(

self

)

Type of the model

value()

dict[str, neml2.Tensor] value	(		self,
		dict	arg0 )

value_and_dvalue()

tuple[dict[str, neml2.Tensor], dict[str, dict[str, neml2.Tensor]]] value_and_dvalue	(		self,
		dict	arg0 )

zero_undefined_input()

None zero_undefined_input

(

self

)

Zero undefined input variables