Budget-limited distribution learning in multifidelity problems

Multifidelity methods are widely used for estimating quantities of interest (QoIs) in computational science by employing numerical simulations of differing costs and accuracies. Many methods approximate numerical-valued statistics that represent only limited information about the QoIs. In this paper, we generalize the ideas in \cite{xu2021bandit} to develop a multifidelity method that approximates the full distribution of scalar-valued QoIs. The main advantage of our approach compared to alternative methods is that we require no particular relationships among the high and lower-fidelity models (e.g. model hierarchy), and we do not assume any knowledge of model statistics including correlations and other cross-model statistics before the procedure starts. Under suitable assumptions in the framework above, we achieve provable 1-Wasserstein metric convergence of an algorithmically constructed distributional emulator via an exploration-exploitation strategy. We also prove that crucial policy actions taken by our algorithm are budget-asymptotically optimal. Numerical experiments are provided to support our theoretical analysis.