  On the basis of input-output time-domain data collected from a complex
simulator, this paper proposes a constructive methodology to infer a
reduced-order linear, bilinear or quadratic time invariant dynamical model
reproducing the underlying phenomena. The approach is essentially based on
linear dynamical systems and approximation theory. More specifically, it
sequentially involves the interpolatory Pencil and Loewner framework, known to
be both very versatile and scalable to large-scale data sets, and a linear
least square problem involving the raw data and reduced internal variables.
With respect to intrusive methods, no prior knowledge on the operator is
needed. In addition, compared to the traditional non-intrusive operator
inference ones, the proposed approach alleviates the need of measuring the
original full-order model internal variables. It is thus applicable to a wider
application range than standard intrusive and non-intrusive methods. The
rationale is successfully applied on a large eddy simulation of a pollutants
dispersion case over an airport area involving multi-scale and multi-physics
dynamical phenomena. Despite the simplicity of the resulting low complexity
model, the proposed approach shows satisfactory results to predict the
pollutants plume pattern while being significantly faster to simulate.
