custom_ml_surrogate_model

Module Contents

Classes

CdModel	The Node class is a base type for nodes in a Directed
ClModel	The Node class is a base type for nodes in a Directed

class custom_ml_surrogate_model.CdModel(*args, **kwargs)

Bases: csdl.CustomExplicitOperation

The Node class is a base type for nodes in a Directed Acyclic Graph (DAG) that represents the computation to be performed during model evaluation.

compute(inputs, outputs)

Define outputs as an explicit function of the inputs

Example

```py def compute(self, inputs, outputs):

outputs[‘L’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] outputs[‘D’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’]

```

compute_derivatives(inputs, derivatives)

User defined method to compute partial derivatives for this operation

Example

```py def compute(self, inputs, outputs):

outputs[‘L’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] outputs[‘D’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’]

def compute_derivatives(self, inputs, derivatives):

derivatives[‘L’, ‘Cl’] = 1/2 * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘L’, ‘rho’] = 1/2 * inputs[‘Cl’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘L’, ‘V’] = inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’] * inputs[‘S’] derivatives[‘L’, ‘S’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2

derivatives[‘D’, ‘Cd’] = 1/2 * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘D’, ‘rho’] = 1/2 * inputs[‘Cd’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘D’, ‘V’] = inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’] * inputs[‘S’] derivatives[‘D’, ‘S’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2

```

define()

User defined method to define custom operation

Example

def define(self):
    self.add_input('Cl')
    self.add_input('Cd')
    self.add_input('rho')
    self.add_input('V')
    self.add_input('S')
    self.add_output('L')
    self.add_output('D')

    # declare derivatives of all outputs wrt all inputs
    self.declare_derivatives('*', '*'))

initialize()

User defined method to declare parameter values. Parameters are compile time constants (neither inputs nor outputs to the model) and cannot be updated at runtime. Parameters are intended to make a CustomOperation subclass definition generic, and therefore reusable. The example below shows how a CustomOperation subclass definition uses parameters and how the user can set parameters when constructing the example CustomOperation subclass. Note that the user never instantiates nor inherits directly from the CustomOperation base class.

Example

```py # in this example, we inherit from ExplicitOperation, but # the user can also inherit from ImplicitOperation class Example(ExplicitOperation):

def initialize(self):

self.parameters.declare(‘in_name’, types=str) self.parameters.declare(‘out_name’, types=str)

def define(self):

# use parameters declared in initialize in_name = self.parameters[‘in_name’] out_name = self.parameters[‘out_name’]

self.add_input(in_name) self.add_output(out_name) self.declare_derivatives(out_name, in_name)

# define run time behavior by defining other methods…

# compile using Simulator imported from back end… sim = Simulator(

Example(

in_name=’x’, out_name=’y’,

),

)

class custom_ml_surrogate_model.ClModel(*args, **kwargs)

Bases: csdl.CustomExplicitOperation

The Node class is a base type for nodes in a Directed Acyclic Graph (DAG) that represents the computation to be performed during model evaluation.

compute(inputs, outputs)

Define outputs as an explicit function of the inputs

Example

```py def compute(self, inputs, outputs):

outputs[‘L’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] outputs[‘D’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’]

```

compute_derivatives(inputs, derivatives)

User defined method to compute partial derivatives for this operation

Example

```py def compute(self, inputs, outputs):

outputs[‘L’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] outputs[‘D’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’]

def compute_derivatives(self, inputs, derivatives):

derivatives[‘L’, ‘Cl’] = 1/2 * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘L’, ‘rho’] = 1/2 * inputs[‘Cl’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘L’, ‘V’] = inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’] * inputs[‘S’] derivatives[‘L’, ‘S’] = 1/2 * inputs[‘Cl’] * inputs[‘rho’] * inputs[‘V’]**2

derivatives[‘D’, ‘Cd’] = 1/2 * inputs[‘rho’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘D’, ‘rho’] = 1/2 * inputs[‘Cd’] * inputs[‘V’]**2 * inputs[‘S’] derivatives[‘D’, ‘V’] = inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’] * inputs[‘S’] derivatives[‘D’, ‘S’] = 1/2 * inputs[‘Cd’] * inputs[‘rho’] * inputs[‘V’]**2

```

define()

User defined method to define custom operation

Example

def define(self):
    self.add_input('Cl')
    self.add_input('Cd')
    self.add_input('rho')
    self.add_input('V')
    self.add_input('S')
    self.add_output('L')
    self.add_output('D')

    # declare derivatives of all outputs wrt all inputs
    self.declare_derivatives('*', '*'))

initialize()

User defined method to declare parameter values. Parameters are compile time constants (neither inputs nor outputs to the model) and cannot be updated at runtime. Parameters are intended to make a CustomOperation subclass definition generic, and therefore reusable. The example below shows how a CustomOperation subclass definition uses parameters and how the user can set parameters when constructing the example CustomOperation subclass. Note that the user never instantiates nor inherits directly from the CustomOperation base class.

Example

```py # in this example, we inherit from ExplicitOperation, but # the user can also inherit from ImplicitOperation class Example(ExplicitOperation):

def initialize(self):

self.parameters.declare(‘in_name’, types=str) self.parameters.declare(‘out_name’, types=str)

def define(self):

# use parameters declared in initialize in_name = self.parameters[‘in_name’] out_name = self.parameters[‘out_name’]

self.add_input(in_name) self.add_output(out_name) self.declare_derivatives(out_name, in_name)

# define run time behavior by defining other methods…

# compile using Simulator imported from back end… sim = Simulator(

Example(

in_name=’x’, out_name=’y’,

),

)