LibtorchArtificialNeuralNetParameters

Overview

Converts the parameters of a LibtorchArtificialNeuralNet within a LibtorchNeuralNetControl into a vector which can be used for the visualization of the evolution of different parameters over the simulation.

Example Syntax

[Reporters<<<{"href": "../../../syntax/Reporters/index.html"}>>>]
  inactive<<<{"description": "If specified blocks matching these identifiers will be skipped."}>>> = 'nn_parameters'
  [T_reporter]
    type = AccumulateReporter<<<{"description": "Reporter which accumulates the value of a inputted reporter value over time into a vector reporter value of the same type.", "href": "../../reporters/AccumulateReporter.html"}>>>
    reporters<<<{"description": "The reporters to accumulate."}>>> = 'T_max/value control_value/value'
    outputs<<<{"description": "Vector of output names where you would like to restrict the output of variables(s) associated with this object"}>>> = csv_out
  []
  [nn_parameters]
    type = LibtorchArtificialNeuralNetParameters
    control_name = src_control
    execute_on = FINAL
    outputs = json_out
  []
[]

warning

The detailed documentation of this object is only available when Moose is compiled with Libtorch. For instructions on how to compile Moose with Libtorch, visit the general installation webpage or click here.

(moose/test/tests/controls/libtorch_nn_control/read_control.i)

pi = 3.14159265359
period = 0.25
diff_coeff = 0.5
cp = 1.0

[Functions]
  [src_func]
    type = ParsedFunction
    value = "sin(${pi}/${period}*t)"
  []
[]

[Mesh]
  [msh]
    type = GeneratedMeshGenerator
    dim = 2
    nx = 20
    xmin = -0.5
    xmax = 0.5
    ny = 20
    ymin = -0.5
    ymax = 0.5
  []
  [source_domain]
    type = ParsedSubdomainMeshGenerator
    input = msh
    combinatorial_geometry = '(x<0.2 & x>-0.2) & (y<0.2 & y>-0.2)'
    block_id = 1
  []
[]

[Variables]
  [T]
    initial_condition = 1
  []
[]

[Kernels]
  [diffusion]
    type = CoefDiffusion
    variable = T
    coef = ${diff_coeff}
  []
  [source]
    type = BodyForce
    variable = T
    function = src_func
    block = 1
  []
  [anti_source]
    type = BodyForce
    variable = T
    value = 0
    block = 1
  []
  [time_deriv]
    type = CoefTimeDerivative
    Coefficient = ${cp}
    variable = T
  []
[]

[BCs]
  [neumann_rest]
    type = NeumannBC
    variable = T
    boundary = 'left right top bottom'
    value = 0
  []
[]

[Executioner]
  type = Transient
  num_steps = 25
  dt = 0.1
  solve_type = NEWTON
  petsc_options_iname = '-pc_type'
  petsc_options_value = 'lu'
  nl_abs_tol = 1e-12
  line_search = 'none'
[]

[Postprocessors]
  [T_max]
    type = NodalExtremeValue
    variable = T
    execute_on = 'INITIAL TIMESTEP_END'
  []
  [control_value]
    type = LibtorchControlValuePostprocessor
    control_name = src_control
    execute_on = 'INITIAL TIMESTEP_END'
  []
[]

[Controls]
  [src_control]
    type = LibtorchNeuralNetControl
    parameters = "Kernels/anti_source/value"
    responses = 'T_max'
    execute_on = 'TIMESTEP_BEGIN'
  []
[]

[Reporters]
  inactive = 'nn_parameters'
  [T_reporter]
    type = AccumulateReporter
    reporters = 'T_max/value control_value/value'
    outputs = csv_out
  []
  [nn_parameters]
    type = LibtorchArtificialNeuralNetParameters
    control_name = src_control
    execute_on = FINAL
    outputs = json_out
  []
[]

[Outputs]
  [csv_out]
    type = CSV
    execute_on = FINAL
  []
  [json_out]
    type = JSON
    execute_on = FINAL
    execute_system_information_on = NONE
  []
[]