refactor: make batch to be true by default for NNODE

src/ode_solve.jl

@@ -27,11 +27,9 @@ of the physics-informed neural network which is used as a solver for a standard
  the PDE operators. The reverse mode of the loss function is always
  automatic differentiation (via Zygote), this is only for the derivative
  in the loss function (the derivative with respect to time).
-* `batch`: The batch size for the loss computation. Defaults to `false`, which
- means the application of the neural network is done at individual time points one
- at a time. `true` means the neural network is applied at a row vector of values
- `t` simultaneously, i.e. it's the batch size for the neural network evaluations.
- This requires a neural network compatible with batched data.
+* `batch`: The batch size for the loss computation. Defaults to `true`, means the neural network is applied at a row vector of values
+ `t` simultaneously, i.e. it's the batch size for the neural network evaluations. This requires a neural network compatible with batched data. 
+ `false` means which means the application of the neural network is done at individual time points one at a time. 
  This is not applicable to `QuadratureTraining` where `batch` is passed in the `strategy` which is the number of points it can parallelly compute the integrand.
 * `param_estim`: Boolean to indicate whether parameters of the differential equations are learnt along with parameters of the neural network.
 * `strategy`: The training strategy used to choose the points for the evaluations.
@@ -89,7 +87,7 @@ struct NNODE{C, O, P, B, PE, K, AL <: Union{Nothing, Function},
 end
 function NNODE(chain, opt, init_params = nothing;
  strategy = nothing,
- autodiff = false, batch = false, param_estim = false, additional_loss = nothing, kwargs...)
+ autodiff = false, batch = true, param_estim = false, additional_loss = nothing, kwargs...)
  !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  NNODE(chain, opt, init_params, autodiff, batch, strategy, param_estim, additional_loss, kwargs)
 end