Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations

Specialized kernel models are introduced as a plug-and-play solution to significantly accelerate simulations of reactive systems without requiring machine learning expertise from the users. The workflow reduces the need for expensive training data and ensures accuracy even for extrapolation to unseen reaction conditions.

Abstract

Multi-scale modeling allows the description of real reactive systems under industrially relevant conditions. However, its application to rational catalyst and reactor design is hindered by the prohibitively high computational cost associated with the chemical kinetics on the catalyst scale. Here, the computational cost is drastically reduced by introducing goal-oriented kernel models that serve as surrogates for the chemical kinetics. This special model type allows for automated training set design and reliable results, even outside the training region. Therefore, it can be envisioned as a plug-and-play solution for accelerating reactive flow simulations with guaranteed accuracy.

Zum Volltext