*4.1. Sensitivity Analysis*

First, we present the results of the sensitivity analyses, as summarized in the pie charts of Figures 7–9. For each of the three material models, these figures depict the contributions, at two impact velocities, of the parameters to global variance, considering independently the two QoIs: namely, the increments in anvil's radius and length.

**Figure 7.** Global Sensitivity Analysis (GSA) results for the Johnson–Cook (JC) model considering ∆*R* and ∆*L* at 200 and 320 m/s.

**Figure 8.** GSA results for the Zerilli–Armstrong (ZA) model considering ∆*R* and ∆*L* at 200 and 320 m/s.

**Figure 9.** GSA results for the Arrhenius-type model considering ∆*R* and ∆*L* at 200 and 320 m/s.

In all the three models, the pie charts expressing the parameters' influence are slightly different, as expected from a complex experiment. However, the most significant result of the analysis performed is that the most influential parameters of each material model coincide in the four sensitivity figures.

To proceed, we identify for each material model the smallest set of parameters whose combined influence accounts for at least 90% of the total QoI variance in all the tests performed and we summarize these findings in Table 3. These results are useful in two ways. First, they simplify the ensuing Bayesian calibration, limiting the number of hyperparameters for the Gaussian processes and the computations involved in the likelihood calculations. Second, from a quantitative point of view, it can be employed by users of material models in numerical simulations to reveal the most influential parameters in the three laws considered, where most of the calibration efforts should be placed, irrespective of the methodology followed to this end.

**Table 3.** Model parameters accounting for 90% or more of the Quantity of Interest (QoI) variance.

