Provenance-integrated parameter selection and optimization in numerical simulations

Simulations based on partial differential equations (PDEs) are used in a large variety of scenarios, that each come with varying requirements, e.g., in terms of runtime or accuracy. Different numerical approaches to approximate exact solutions exist, that typically contain a multitude of parameters that can be tailored to the problem at hand. We explore how high-level provenance, i.e., provenance that is expensive to capture in a single simulation, can be used to optimize such parameters in future simulations for sufficiently similar problems. Our experiments on one of the key building blocks of PDE simulations underline the potential of this approach.