This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of adaptive sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of the drag coefficient on wind-speed based on AXBT temperature data obtained during Typhoon Fanapi.