  A method for data-driven interpolatory model reduction is presented in this
extended abstract. This framework enables the computation of the transfer
function values at given interpolation points based on time-domain input-output
data only, without explicitly identifying the high-order system. Instead, by
characterizing the set of all systems explaining the data, necessary and
sufficient conditions are given under which all systems in this set share the
same transfer function value at a given interpolation point. After following
this so-called data informativity perspective, reduced-order models can be
obtained by classical interpolation techniques. An example of an electrical
circuit illustrates this framework.
