Methods
Lorenz_Solver$new()
Initialize a solver for a Lorenz system. The solver takes
its own copy of the system as it is at this moment and steps that copy:
set_params() or set_state() on the LorenzSystem
afterwards do not reach the solver. Use the solver's own
$set_params() and $set_state().
Arguments
System_xpExternal pointer to the Lorenz system object.
control_xpExternal pointer to the ODE control object.
methodIntegration method: "rkck" (default, explicit Cash-Karp
RK 4(5)), "dopri" (explicit Dormand-Prince 5(4)) or "rodas"
(implicit RODAS4(3) Rosenbrock, for stiff systems).
Lorenz_Solver$pars()
Parameters of the system this solver steps.
Lorenz_Solver$set_params()
Set the parameters this solver steps with, and re-seed it
at its current state and time as $set_state() would. Call
$reset() as well to start again from the initial state.
Usage
Lorenz_Solver$set_params(params)
Arguments
paramsNumeric vector of system parameters, one entry per parameter
in the system's order (for Lorenz: sigma, R, b).
Lorenz_Solver$time()
Get current solver time.
Lorenz_Solver$state()
Get current solver state.
Lorenz_Solver$set_state()
Set current solver state and time.
Usage
Lorenz_Solver$set_state(y, time)
Arguments
yNumeric state vector.
timeScalar time value.
Lorenz_Solver$times()
Get stored solver times.
Lorenz_Solver$advance_adaptive()
Advance solver using adaptive stepping.
Usage
Lorenz_Solver$advance_adaptive(times)
Arguments
timesNumeric vector of requested output times.
Lorenz_Solver$advance_fixed()
Advance solver using fixed stepping.
Usage
Lorenz_Solver$advance_fixed(times)
Arguments
timesNumeric vector of requested output times.
Lorenz_Solver$advance_euler()
Advance solver using fixed-step forward Euler.
Usage
Lorenz_Solver$advance_euler(times)
Arguments
timesNumeric vector of requested output times.
Lorenz_Solver$step()
Advance solver by one step.
Lorenz_Solver$reset()
Reset solver to its initial state.
Lorenz_Solver$collect()
Get or set history collection behavior.
Usage
Lorenz_Solver$collect(value)
Arguments
valueLogical flag for collect-history behavior.
Lorenz_Solver$history_size()
Return number of stored history entries.
Usage
Lorenz_Solver$history_size()
Lorenz_Solver$history_step()
Return one history entry by index.
Usage
Lorenz_Solver$history_step(i)
Arguments
iInteger index into solver history.
Lorenz_Solver$history()
Return history as a tibble.
Lorenz_Solver$set_target()
Set the calibration target for
$loss_and_gradient().
Usage
Lorenz_Solver$set_target(schedule, target, obs_indices)
Arguments
scheduleNumeric vector: the step times
$loss_and_gradient() replays, one entry per step. Normally a
reference run's $times(), so each evaluation takes the same steps
and the loss is a smooth function of the inputs.
targetNumeric matrix of observed states, one row per entry of
obs_indices and one column per state variable.
obs_indicesInteger indices (1-based) into schedule at which
each row of target was observed.
Lorenz_Solver$loss_and_gradient()
Least-squares loss against the target, and its exact
gradient by reverse-mode automatic differentiation. The schedule is
replayed under this solver's method, except that a
"rodas" solver's is replayed under "rkck": the
Rosenbrock stepper cannot be swept.
Usage
Lorenz_Solver$loss_and_gradient(initial_state = NULL, params = NULL)
Arguments
initial_stateInitial state to evaluate at, or NULL to keep
the system's.
paramsParameters to evaluate at, or NULL to keep the
system's.
Returns
A list with loss (sum of squared differences) and
gradient: d(loss)/d(params), then d(loss)/d(initial_state), for
whichever were given.
Lorenz_Solver$clone()
The objects of this class are cloneable with this method.
Usage
Lorenz_Solver$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.