Perform a numeric simulation using the landscape. The simulation is performed
using a similar algorithm as Glauber dynamics, that the transition possibility
is determined by the difference in the potential function, and the steady-state
distribution is the same as the Boltzmann distribution (if not setting `beta2`

).
Note that, the conditional transition possibility of this simulation
may be different from Glauber dynamics or other simulation methods.

## Usage

```
simulate_Isingland(l, ...)
# S3 method for class '`2d_Isingland`'
simulate_Isingland(
l,
mode = "single",
initial = 0,
length = 100,
beta2 = l$beta,
...
)
# S3 method for class '`2d_Isingland_matrix`'
simulate_Isingland(
l,
mode = "single",
initial = 0,
length = 100,
beta2 = NULL,
...
)
```

## Arguments

- l
An

`Isingland`

object constructed with`make_2d_Isingland()`

or`make_2d_Isingland_matrix()`

.- ...
Not in use.

- mode
One of

`"single"`

,`"distribution"`

. Do you want to simulate the state of a single system stochastically or simulate the distribution of the states?`"single"`

is used by default.- initial
An integer indicating the initial number of active nodes for the simulation. Float numbers will be converted to an integer automatically.

- length
An integer indicating the simulation length.

- beta2
The \(beta\) value used for simulation. By default use the same value as for landscape construction. Manually setting this value can make the system easier or more difficult to transition to another state, but will alter the steady-state distribution as well.

## Value

A `sim_Isingland`

object with the following components:

`output`

A tibble of the simulation output.`landscape`

The landscape object supplied to this function.`mode`

A character representing the mode of the simulation.

## Details

In each simulation step, the system can have one more active node, one less active node, or the same number of active nodes (if possible; so if all nodes are already active then it is not possible to have one more active node). The possibility of the three cases is determined by their potential function:

$$P(n_{t}=b|n_{t-1}=a) = \frac{e^{-\beta U(b)}}{\sum_{i \in \{a-1,a,a+1\},0\leq i\leq N}e^{-\beta U(i)}}, \ \mathrm{if} \ b\in\{a-1,a,a+1\}\ \&\ 0\leq i\leq N; 0, \mathrm{otherwise},$$

where \(n_{t}\) is the number of active nodes at the time \(t\), and \(U(n)\) is the potential function.