  A framework is proposed for generative models as a basis for digital twins or
mirrors of structures. The proposal is based on the premise that deterministic
models cannot account for the uncertainty present in most structural modelling
applications. Two different types of generative models are considered here. The
first is a physics-based model based on the stochastic finite element (SFE)
method, which is widely used when modelling structures that have material and
loading uncertainties imposed. Such models can be calibrated according to data
from the structure and would be expected to outperform any other model if the
modelling accurately captures the true underlying physics of the structure. The
potential use of SFE models as digital mirrors is illustrated via application
to a linear structure with stochastic material properties. For situations where
the physical formulation of such models does not suffice, a data-driven
framework is proposed, using machine learning and conditional generative
adversarial networks (cGANs). The latter algorithm is used to learn the
distribution of the quantity of interest in a structure with material
nonlinearities and uncertainties. For the examples considered in this work, the
data-driven cGANs model outperform the physics-based approach. Finally, an
example is shown where the two methods are coupled such that a hybrid model
approach is demonstrated.
