Statistical moments for simulation calibration with model-bridge

Computer simulations are actively used for analyzing complex phenomena, especially in fields where access to their real-world counterparts is not feasible, such as physics, chemistry, material science, and others. The key to executing successful simulations is making sure that the parameters of a simulator reflect real-world scenarios, a tedious and error-prone effort addressed through simulation calibration. Recently, several methods have been proposed to automatize this task by learning from previously calibrated simulations using a model-bridge paradigm: a complex simulation is replaced by a simpler surrogate model, which can then be bridged to the calibrated simulation parameters. However, designing the surrogate model is a non-trivial problem involving trade-offs between simplicity of representation, interpretability and calibration accuracy, as well as the complexity of the bridge model required to map the surrogate to calibrated parameters. Further, while effective, such approaches can be non-intuitive for practitioners due to their distance from the simulation. In this paper, we view a simulation as a distribution of output variables, which can be easily represented by statistical moments. This yields a very simple and interpretable surrogate that can be bridged to calibrated parameters with a simple linear regression. We show that our method outperforms previous approaches, in terms of calibration accuracy and time, through experiments on simulations of turbulent flow dynamics and synthetic signals.