This paper presents a methodology for identifying important discipline couplings in multicomponent engineering systems. Coupling among disciplines contributes significantly to the computational cost of analyzing a system and can become particularly burdensome when coupled analyses are embedded within a design or optimization loop. In many cases, disciplines may be weakly coupled, so that some of the coupling or interaction terms can be neglected without significantly impacting the accuracy of the system output. Typical practice derives such approximations in an ad hoc manner using expert opinion and domain experience. This work proposes a new approach that formulates an optimization problem to find a model that optimally balances accuracy of the model outputs with the sparsity of the discipline couplings. An adaptive sequential Monte Carlo sampling-based technique is used to efficiently search the combinatorial model space of different discipline couplings. An algorithm for selecting an optimal model is presented and illustrated in a fire-detection satellite model and a turbine engine cycle analysis model.