  Multiscale models of materials, consisting of upscaling discrete simulations
to continuum models, are unique in their capability to simulate complex
materials behavior. The fundamental limitation in multiscale models is the
presence of uncertainty in the computational predictions delivered by them. In
this work, a sequential multiscale model has been developed, incorporating
discrete dislocation dynamics (DDD) simulations and a strain gradient
plasticity (SGP) model to predict the size effect in plastic deformations of
metallic micro-pillars. The DDD simulations include uniaxial compression of
micro-pillars with different sizes and over a wide range of initial dislocation
densities and spatial distributions of dislocations. An SGP model is employed
at the continuum level that accounts for the size-dependency of flow stress and
hardening rate. Sequences of uncertainty analyses have been performed to assess
the predictive capability of the multiscale model. The variance-based global
sensitivity analysis determines the effect of parameter uncertainty on the SGP
model prediction. The multiscale model is then constructed by calibrating the
continuum model using the data furnished by the DDD simulations. A Bayesian
calibration method is implemented to quantify the uncertainty due to
microstructural randomness in discrete dislocation simulations (density and
spatial distributions of dislocations) on the macroscopic continuum model
prediction (size effect in plastic deformation). The outcomes of this study
indicate that the discrete-continuum multiscale model can accurately simulate
the plastic deformation of micro-pillars, despite the significant uncertainty
in the DDD results. Additionally, depending on the macroscopic features
represented by the DDD simulations, the SGP model can reliably predict the size
effect in plasticity responses of the micropillars with below 10% of error
