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Structure

structure

Structure discipline for the Sobieski's SSBJ use case.

Classes

JAXSobieskiStructure 

JAXSobieskiStructure()

Bases: BaseJAXSobieskiDiscipline

Structure discipline for the Sobieski's SSBJ use case.

Initialize the JAXDiscipline.

Source code in src/gemseo_jax/problems/sobieski/structure.py 
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def __init__(self) -> None:  # noqa: D107
    super().__init__()
    self.__bound1 = array([0.25, 0.25, 0.25, 0.25])
    self.__flag1 = array(
        [[0.95, 1.0, 1.1], [1.05, 1.0, 0.9], [1.05, 1.0, 0.9], [1.05, 1.0, 0.95]],
    )
    self.__s_initial_for_wing_twist = array(
        [
            self.x_initial,
            self.half_span_initial,
            self.aero_center_initial,
            self.lift_initial,
        ],
    )
    self.__s_initial_for_wing_weight = array([self.x_initial])
    self.__bound_secthick = array([0.008])
    self.__flag_secthick = array([[0.95, 1.0, 1.05]])
    self.__flag_stress = array(
        [
            [1.05, 1.0, 0.9],
            [0.95, 1.0, 1.05],
            [1.05, 1.0, 0.9],
            [0.95, 1.0, 1.05],
            [0.95, 1.0, 1.05],
        ],
    )
    self.__s_initial_for_constraints = array(
        [
            self.tc_initial,
            self.lift_initial,
            self.x_initial,
            self.half_span_initial,
            self.aero_center_initial,
        ],
    )

    self.__loc_ones = ones(5) * 0.1
    self.default_input_data["c_0"] = np_array([self.constants[0]])
    self.default_input_data["c_1"] = np_array([self.constants[1]])
    self.default_input_data["c_2"] = np_array([self.constants[2]])
    self._fww_coeff = 5 / 18 * 2 / 3 * 42.5
    self._a0_y12 = 1.0
    self._ai_y12 = array([0.3, -0.3, -0.3, -0.2])
    self.__aij_y12 = array(
        [
            [0.2, 0.0794, 0.16304, 0.1846],
            [0.0794, -0.2, -0.12714, -0.1487],
            [0.16304, -0.12714, -0.2, -0.07314],
            [0.1846, -0.1487, -0.07314, 0.0],
        ],
    )
    self.__a0_f0 = 1.0
    self.__ai_f0 = array([6.25])
    self.__aij_f0 = array([[0.0]])
    self.__a0_g1 = [1.0, 1.0, 1.0, 1.0, 1.0]
    self.__ai_g1 = [
        array([-0.75, 0.5, -0.75, 0.5, 0.5]),
        array([-0.5, 1 / 3, -0.5, 1 / 3, 1 / 3]),
        array([-0.375, 0.25, -0.375, 0.25, 0.25]),
        array([-0.3, 0.2, -0.3, 0.2, 0.2]),
        array([-0.25, 1 / 6, -0.25, 1 / 6, 1 / 6]),
    ]
    self.__aij_g1 = [
        array(
            [
                [-1.25, -0.49625, -1.019, -1.15375, -0.1385],
                [-0.49625, 0.0, 0.0, 0.0, 0.0],
                [-1.019, 0.0, -1.25, -0.457125, -0.002375],
                [-1.15375, 0.0, -0.457125, 0.0, 0.0],
                [-0.1385, 0.0, -0.002375, 0.0, 0.0],
            ],
        ),
        array(
            [
                [-0.55555556, -0.22055556, -0.45288889, -0.51277778, -0.06155556],
                [-0.22055556, 0.0, 0.0, 0.0, 0.0],
                [-0.45288889, 0.0, -0.55555556, -0.20316667, -0.00105556],
                [-0.51277778, 0.0, -0.20316667, 0.0, 0.0],
                [-0.06155556, 0.0, -0.00105556, 0.0, 0.0],
            ],
        ),
        array(
            [
                [-0.3125, -0.1240625, -0.25475, -0.2884375, -0.034625],
                [-0.1240625, 0.0, 0.0, 0.0, 0.0],
                [-0.25475, 0.0, -0.3125, -0.11428125, -0.00059375],
                [-0.2884375, 0.0, -0.11428125, 0.0, 0.0],
                [-0.034625, 0.0, -0.00059375, 0.0, 0.0],
            ],
        ),
        array(
            [
                [-0.2, -0.0794, -0.16304, -0.1846, -0.02216],
                [-0.0794, 0.0, 0.0, 0.0, 0.0],
                [-0.16304, 0.0, -0.2, -0.07314, -0.00038],
                [-0.1846, 0.0, -0.07314, 0.0, 0.0],
                [-0.02216, 0.0, -0.00038, 0.0, 0.0],
            ],
        ),
        array(
            [
                [-0.13888889, -0.05513889, -0.11322222, -0.12819444, -0.01538889],
                [-0.05513889, 0.0, 0.0, 0.0, 0.0],
                [-0.11322222, 0.0, -0.13888889, -0.05079167, -0.00026389],
                [-0.12819444, 0.0, -0.05079167, 0.0, 0.0],
                [-0.01538889, 0.0, -0.00026389, 0.0, 0.0],
            ],
        ),
    ]
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 
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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 
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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 
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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()