from functools import partial
from typing import Sequence
import equinox.internal as eqxi
import jax
from fdtdx.config import SimulationConfig
from fdtdx.fdtd.container import ObjectContainer, SimulationState
from fdtdx.fdtd.update import add_interfaces, update_detector_states, update_E_reverse, update_H_reverse
def cond_fn(state, start_time_step: int) -> bool:
time_step = state[0]
return time_step > start_time_step
[docs]
def full_backward(
state: SimulationState,
objects: ObjectContainer,
config: SimulationConfig,
key: jax.Array,
record_detectors: bool,
reset_fields: bool,
start_time_step: int = 0,
) -> SimulationState:
"""Perform full backward FDTD propagation from current state to start time.
Uses a while loop to repeatedly call backward() until reaching start_time_step.
Leverages time-reversibility of Maxwell's equations.
Args:
state (SimulationState): Current simulation state tuple (time_step, arrays)
objects (ObjectContainer): Container with simulation objects (sources, detectors, etc)
config (SimulationConfig): Simulation configuration parameters
key (jax.Array): JAX PRNG key for random operations
record_detectors (bool): Whether to record detector states
reset_fields (bool): Whether to reset fields after each step
start_time_step (int, optional): Time step to propagate back to (default: 0)
Returns:
SimulationState: Final state after backward propagation
"""
s0 = eqxi.while_loop(
cond_fun=partial(cond_fn, start_time_step=start_time_step),
body_fun=partial(
backward,
config=config,
objects=objects,
key=key,
record_detectors=record_detectors,
reset_fields=reset_fields,
),
init_val=state,
kind="lax",
)
return s0
def backward(
state: SimulationState,
config: SimulationConfig,
objects: ObjectContainer,
key: jax.Array,
record_detectors: bool,
reset_fields: bool,
fields_to_reset: Sequence[str] = ("E", "H"),
) -> SimulationState:
"""Perform one step of backward FDTD propagation.
Updates fields from time step t to t-1 using time-reversed Maxwell's equations.
Handles interfaces, field updates, optional field resetting, and detector recording.
Args:
state (SimulationState): Current simulation state tuple (time_step, arrays)
config (SimulationConfig): Simulation configuration parameters
objects (ObjectContainer): Container with simulation objects (sources, detectors, etc)
key (jax.Array): JAX PRNG key for random operations
record_detectors (bool): Whether to record detector states
reset_fields (bool): Whether to reset fields after updates
fields_to_reset (Sequence[str], optional): Which fields to reset if reset_fields is True. Defaults to ("E", "H").
Returns:
SimulationState: Updated state after one backward step
"""
time_step, arrays = state
time_step = time_step - 1
arrays = add_interfaces(
time_step=time_step,
arrays=arrays,
objects=objects,
config=config,
key=key,
)
H = arrays.fields.H
arrays = update_H_reverse(
time_step=time_step,
arrays=arrays,
config=config,
objects=objects,
)
arrays = update_E_reverse(
time_step=time_step,
arrays=arrays,
config=config,
objects=objects,
)
if reset_fields:
new_fields = {f: getattr(arrays.fields, f) for f in fields_to_reset}
for boundary in objects.boundary_objects:
new_fields = boundary.apply_field_reset(new_fields)
for name, f in new_fields.items():
arrays = arrays.aset(f"fields->{name}", f)
if record_detectors:
arrays = update_detector_states(
time_step=time_step,
arrays=arrays,
objects=objects,
config=config,
H_prev=H,
inverse=True,
)
next_state = (time_step, arrays)
return next_state
