Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations

Many engineering applications rely on simulations based on partial differential equations. Different numerical schemes to approximate solutions exist. These schemes typically require setting parameters to appropriately model the problem at hand. We study the problem of parameter selection for applications that rely on simulations, where standard methods like grid search are computationally prohibitive. Our solution supports engineers in setting parameters based on knowledge gained through analyzing metadata acquired while partially executing specific simulations. Selecting these so-called farming runs of simulations is guided by an optimization algorithm that leverages the acquired knowledge. Experiments demonstrate that our solution outperforms state-ofthe-art approaches and generalizes to a wide range of application settings.