An Approach to Integrate Domain Knowledge into Feature Engineering to Enhance Data-Driven Surrogate Models of Simulations

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Surrogate models are approximations of time-consuming computer simulations. These approximations may be derived by applying machine learning algorithms on a training set of simulation data. Due to the long runtime of these simulations, the amount of available simulation data to train surrogate models is however limited. This leads to a subpar performance of the resulting surrogate models. Here, feature engineering is important to improve the prediction performance of these models. However, automated feature engineering often results in surrogate models that no longer adhere to laws of physics. Therefore, we present an approach to integrate domain knowledge into feature engineering for the data-driven creation of surrogate models. This reduces the prediction error of surrogate models and ensures they adhere to laws of physics and are thus more reliable. We validate our approach with a prototypical implementation and a use case for simulations in virtual product development, and we show that our approach significantly improves the prediction quality of the resulting surrogate models.