Minimizing Emulator Uncertainty

Emulators are crucial tools for reducing the computational burden of studying parameter dependence in complex models. By approximating the behavior of these models, emulators enable faster analyses, allowing for the exploration of large parameter spaces and extensive studies. The quality of these emulations, however, is highly dependent on the choice of training points and the properties of the data. Various strategies for optimizing these aspects are being discussed and compared.