antcal.model.slotted_patch

Slotted Patch.

Reference: A. Papathanasopoulos, P. A. Apostolopoulos and Y. Rahmat-Samii, “Optimization Assisted by Neural Network-Based Machine Learning in Electromagnetic Applications,” IEEE Transactions on Antennas and Propagation, Jan. 2023, doi: 10.1109/TAP.2023.3269883.

Module Contents

Functions

check_constrains	Return False if dimensions are invalid.
convert_to_variables
create_slotted_patch
solve_sync	Synchronously solve and return solution data.
solve	Asynchronously solve and return solution data.
obj_fn_sync	Object function (synchronous).
obj_fn	Object function (asynchronous).

Data

N_DIMS_SLOTTED_PATCH
VAR_BOUNDS	(lower_bounds, upper_bounds)
SUGGESTED_PARAMS	Suggested parameters from the paper

API

antcal.model.slotted_patch.N_DIMS_SLOTTED_PATCH

10

antcal.model.slotted_patch.VAR_BOUNDS

(None, [70.0, 70.0, 10.0, 10.0, 50.0, 10.0, 70.0, 35.0, 30.0, 10.0])

(lower_bounds, upper_bounds)

antcal.model.slotted_patch.SUGGESTED_PARAMS

None

Suggested parameters from the paper

antcal.model.slotted_patch.check_constrains(v: numpy.typing.NDArray[numpy.float32]) → bool

Return False if dimensions are invalid.

antcal.model.slotted_patch.convert_to_variables(v: numpy.typing.NDArray[numpy.float32]) → dict[str, str]

antcal.model.slotted_patch.create_slotted_patch(hfss: pyaedt.hfss.Hfss, variables: dict[str, str]) → None

antcal.model.slotted_patch.solve_sync(hfss: pyaedt.hfss.Hfss) → pyaedt.modules.solutions.SolutionData

Synchronously solve and return solution data.

async antcal.model.slotted_patch.solve(hfss: pyaedt.hfss.Hfss) → pyaedt.modules.solutions.SolutionData

Asynchronously solve and return solution data.

antcal.model.slotted_patch.obj_fn_sync(hfss: pyaedt.hfss.Hfss, v: numpy.typing.NDArray[numpy.float32]) → numpy.float32

Object function (synchronous).

async antcal.model.slotted_patch.obj_fn(aedt_queue: asyncio.Queue[pyaedt.hfss.Hfss], v: numpy.typing.NDArray[numpy.float32]) → numpy.float32

Object function (asynchronous).