Chain

chain

Chain of the four disciplines of the Sobieski's SSBJ use case.

Classes

JAXSobieskiChain

JAXSobieskiChain()

Bases: JAXChain

Chain of the four disciplines of the Sobieski's SSBJ use case.

Initialize the JAXDiscipline.

Source code in src/gemseo_jax/problems/sobieski/chain.py

def __init__(self) -> None:  # noqa: D102 D107
    disciplines = [
        JAXSobieskiAerodynamics(),
        JAXSobieskiStructure(),
        JAXSobieskiPropulsion(),
        JAXSobieskiMission(),
    ]
    super().__init__(disciplines)
    self.default_input_data.update(
        SobieskiProblem().get_default_inputs(self.input_grammar.names)
    )
    self.add_differentiated_inputs(["x_shared", "x_1", "x_2", "x_3"])
    self.add_differentiated_outputs(["y_4", "g_1", "g_2", "g_3"])
    self.compile_jit()

Attributes

jax_out_func instance-attribute

jax_out_func: Callable[[DataType], DataType] = function

The JAX function to compute the outputs from the inputs.

Classes

DifferentiationMethod

Bases: StrEnum

The method to compute the Jacobian.

Functions

add_differentiated_inputs

add_differentiated_inputs(
    input_names: Iterable[str] = (),
) -> None

Add the inputs with respect to which to differentiate the outputs.

The inputs that do not represent continuous numbers are filtered out.

Parameters:

• input_names (Iterable[str], default: () ) –

  The input variables with respect to which to differentiate the outputs. If empty, use all the inputs.

Raises:

• ValueError –

  When an input name is not the name of a discipline input.

Notes

The Jacobian is also filtered to view non-differentiated static.

Source code in src/gemseo_jax/jax_discipline.py

def add_differentiated_inputs(
    self,
    input_names: Iterable[str] = (),
) -> None:
    """
    Notes:
        The Jacobian is also filtered to view non-differentiated static.
    """  # noqa: D205, D212, D415
    old_differentiated_inputs = self._differentiated_input_names.copy()
    super().add_differentiated_inputs(input_names=input_names)
    refilter = any(
        input_name not in old_differentiated_inputs
        for input_name in self._differentiated_input_names
    )
    if refilter:
        self._filter_jacobian()

add_differentiated_outputs

add_differentiated_outputs(
    output_names: Iterable[str] = (),
) -> None

Add the outputs to be differentiated.

The outputs that do not represent continuous numbers are filtered out.

Parameters:

• output_names (Iterable[str], default: () ) –

  The outputs to be differentiated. If empty, use all the outputs.

Raises:

• ValueError –

  When an output name is not the name of a discipline output.

Notes

The Jacobian is also filtered to view non-differentiated static.

Source code in src/gemseo_jax/jax_discipline.py

def add_differentiated_outputs(
    self,
    output_names: Iterable[str] = (),
) -> None:
    """
    Notes:
        The Jacobian is also filtered to view non-differentiated static.
    """  # noqa: D205, D212, D415
    old_differentiated_outputs = self._differentiated_output_names.copy()
    super().add_differentiated_outputs(output_names=output_names)
    refilter = any(
        output_name not in old_differentiated_outputs
        for output_name in self._differentiated_output_names
    )
    if refilter:
        self._filter_jacobian()

compile_jit

compile_jit(pre_run: bool = True) -> None

Apply jit compilation over function and jacobian.

Parameters:

• pre_run (bool, default: True ) –

  Whether to call jitted callables once to trigger compilation and log times.

Source code in src/gemseo_jax/jax_discipline.py

def compile_jit(
    self,
    pre_run: bool = True,
) -> None:
    """Apply jit compilation over function and jacobian.

    Args:
        pre_run: Whether to call jitted callables once to trigger compilation and
            log times.
    """
    self.jax_out_func = jit(self.jax_out_func)
    self.__jax_jac_func = jit(self.__jax_jac_func)
    if pre_run:
        self._jit_pre_run()