Characteristic properties of elastomers including their fatigue behavior and wear resistance can be tailored by embedding them with filler particles. Along with enhancing the overall properties of the system, filler particles also induce some inelastic effects including the Mullins and the Payne effect. In this paper, computational modeling using finite element methods has been employed to study the mechanical behavior of such systems. A nonlinear model is applied, to predict the macroscopic large deformation behavior, with morphology evolution and deformation occurring at the microscopic level, using the representative volume element (RVE) approach. The approach is based on a micro mechanically motivated hyper-elastic constitutive model, describing the elastomeric matrix within the RVE. In order to simulate the breakdown and reaggregation of filler networks, effects like the change in the glass transition temperature in the vicinity of filler particles, are incorporated in the system. The implementation is designed to be robust, for accommodating large rotations and stretches of the matrix local to, and between, the nanoparticles. The nanocomposite microstructure is reconstructed at the RVE level using a random particle generation algorithm, with the assumption of periodicity. Computational experiments using this methodology enable prediction of the strain softening behavior of filled elastomers, observed experimentally, and to understand the behavior of the interphase between two filler particles and its effect on the characteristic material behavior of filled elastomers.