  In modern computer experiment applications, one often encounters the
situation where various models of a physical system are considered, each
implemented as a simulator on a computer. An important question in such a
setting is determining the best simulator, or the best combination of
simulators, to use for prediction and inference. Bayesian model averaging (BMA)
and stacking are two statistical approaches used to account for model
uncertainty by aggregating a set of predictions through a simple linear
combination or weighted average. Bayesian model mixing (BMM) extends these
ideas to capture the localized behavior of each simulator by defining
input-dependent weights. One possibility is to define the relationship between
inputs and the weight functions using a flexible non-parametric model that
learns the local strengths and weaknesses of each simulator. This paper
proposes a BMM model based on Bayesian Additive Regression Trees (BART). The
proposed methodology is applied to combine predictions from Effective Field
Theories (EFTs) associated with a motivating nuclear physics application.
