Out-of-range shortcut predictions might, however, be checked for consistency: For that, the LMA can be interpreted as an error by subtracting the left-from the right-hand side of eq. ( ). Thus, it offers an easy (and quick to calculate) way to check whether any state is close to equilibrium with a certain tolerance defined by the user. Even if a prediction does not fall in the trained range it might still be suited for use according to the set tolerance. If that is not the case, the prediction may still be well suited as an initial guess for conventional stoichiometric or non-stoichiometric calculation routines. Various dynamic resampling strategies based on this principle are described in literature to speed up inference, dynamically increase performance in-or outside the pretrained range or reduce memory footprint . Our proposed way of dynamically deciding whether or how to use the shortcut model predictions is displayed in Figure .