Update docs/src/inverse_problems/optimization_ode_param_fitting.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/inverse_problems/optimization_ode_param_fitting.md

@@ -48,7 +48,7 @@ To `build_loss_objective` we provide the following arguments:
 - `L2Loss(data_ts, data_vals)`: Defines the loss function. While [other alternatives](https://docs.sciml.ai/DiffEqParamEstim/stable/getting_started/#Alternative-Cost-Functions-for-Increased-Robustness) are available, `L2Loss` is the simplest one (measuring the sum of squared distances between model simulations and data measurements). Its first argument is the time points at which the data is collected, and the second is the data's values.
 - `Optimization.AutoForwardDiff()`: Our choice of [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation) framework.
 
-Furthermore, we can pass any number of additional optional arguments, these are then passed to the internal `solve()` function (that are used to simulate our ODE). Here we provide the following additional arguments:
+Furthermore, we can pass any number of additional optional arguments, these are then passed to the internal `solve()` function (which is used to solve our ODE). Here we provide the following additional arguments:
 - `maxiters=10000`: If the ODE integrator takes a very large number of steps, that is typically the sign of a very poor fit. Reducing the `maxiters` threshold reduces the time we waste on evaluating such models. 
 - `verbose=false`: The simulation of models with highly unsuitable parameter sets typically generate various warnings (such as simulation terminations due to reaching the `maxiters` value). To avoid an overflow of such (here unnecessary) warnings as we evaluate a large number of parameter sets, we turn warnings off.
 - `save_idxs=4`: The measured species (*P*) is the 4th species in our species vector (`species(rn)`). To ensure that the concentration of the right species is evaluated against the data, we set the numeric integrator to only save the value of this species.