“…The in-situ adaptive tabulation [13,19,21,22,[24][25][26] builds such a map during the simulation by exploiting prior solutions to estimate new queries. In contrast, surrogate models like splines or the (error-based modified) Shepard interpolation approach map precomputed solutions to accelerate reactor simulations [2, 15,16,18,20,[27][28][29][30][31] or even spatial subsystems of the reactor [32,33] and breakthrough curves [34]. Lately, primarily machine learning techniques like random forests [35,36] or neural networks [2, 3,37] have been used for accurate predictions of steady-state surface kinetics because they can overcome the so-called curse of dimensionality [38], i.e., the exponentially increasing difficulty to learn high-dimensional data.…”