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We develop a method for efficient prediction of linear and nonlinear rheology of polydisperse polymers by judicious selection of a small number of representative polymer molecules from the large ensemble of chains comprising the molecular weight and branching distribution. Specifically, we use a numerical inversion of the double reptation model to select five or six representative molecular species to optimally fit the linear rheology of commercial polymers and then use the regressed parameters to compute the nonlinear rheology via the Rolie-double-poly (RDP) model. The method is tested for several model systems, namely, two polydisperse linear polystyrene (PS) samples described in Shivokhin et al. [Polym. Eng. Sci. 56, 1012–1020 (2016)] and Münstedt [J. Rheol. 24, 847–867 (1980)], a commercial linear low density polyethylene (LLDPE), and a low density polyethylene (LDPE) whose rheological properties are measured in the current study. For the linear polymers including the PS samples and the LLDPE, the predictions of the "representative" RDP model of the start-up extensional rheology are comparable to those of the "full" RDP model based on all the species drawn from the gas permeation chromatography characterization. The method then successfully predicts the linear rheology of blends of LLDPE and LDPE using the same representative molecules found by fitting each of the pure polymers. Further fitting the extensional rheology of the LDPE requires using the "priorities" q i and stretch relaxation times τ s , i of the representative molecules as adjustable parameters, whose values are then held fixed when predicting the extensional rheology of blends of the LDPE with the LLDPE roughly as successfully as does branch-on-branch model. The reduction in the number of representative polymer species offers new opportunities for faster simulations of flowing polymers, as well as for the prediction of segmental orientation to be used in the modeling of flow-induced crystallization. [ABSTRACT FROM AUTHOR]