This proposal is concerned with the development of novel methodologies (including identification and validation strategies), stochastic representations and numerical methods in stochastic micromechanical modeling of nonlinear microstructures and imperfect interfaces. For the sake of feasibility, the applications will specifically focus on the modeling of hyperelastic microstructures and materials exhibiting surface effects and containing nano-inhomogeneities (such as nanoreinforcements and nanopores). For the case of nonlinear microstructures, the project aims at developing relevant probabilistic models for quantities of interests at both the microscale and mesoscale. The consideration of the latter turns out to be especially suitable for random nonlinear microstructures (such as living tissues) for which the scale separation, which is usually assumed in nonlinear homogenization, cannot be stated. Random variable and random field models for strain-energy functions will be constructed by invoking the maximum entropy principle and propagated through stochastic nonlinear homogenization techniques. A complete methodology for identifying the proposed representations will be further introduced and validated on a simulated database. Concerning the imperfect interface modeling, one may note that surface effects are usually taken into account by retaining an interface model (such as the widely used membrane-type model) involving several assumptions such as those related to the mechanical description of the membrane. Such arbitrary choices certainly generate model uncertainties which may be critical while propagated to coarsest scales and which may therefore penalize the predictive capabilities of the associated multiscale approaches. In this project, we propose to tackle the issue of model uncertainties in multiscale analysis of random microstructures with nano-heterogeneities by constructing nonparametric probabilistic representations for the homogenized properties. A complementary aspect is the construction of robust random generators, able to simulate random variables taking their values in given subspaces defined by inequality constraints and non-Gaussian random fields. Whereas such random fields can typically be generated making use of point-wise polynomial chaos expansions, the preservation of the statistical dependence is hardly achievable with the currently available techniques. In this proposal, we will subsequently address the construction of new random generators relying on the definition of families of Itô stochastic differential equations. Such generators are intended to depend on a limited number of parameters (independent of the probabilistic dimension), for which tuning guidelines will be provided. The proposed models will clearly go a step beyond what is currently done in deterministic mechanics for such materials and the expected results are in the forefront of the ongoing developments within the scopes of uncertainty quantification and material science. In addition, it worth pointing out that such theoretical derivations are absolutely required in order to support the current new developments of 3D-fields measurements and image processing at the microscale of complex materials.