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On the basis of input-output time-domain data collected from a complexsimulator, this paper proposes a constructive methodology to infer areduced-order linear, bilinear or quadratic time invariant dynamical modelreproducing the underlying phenomena. The approach is essentially based onlinear dynamical systems and approximation theory. More specifically, itsequentially involves the interpolatory Pencil and Loewner framework, known tobe both very versatile and scalable to large-scale data sets, and a linearleast square problem involving the raw data and reduced internal variables.With respect to intrusive methods, no prior knowledge on the operator isneeded. In addition, compared to the traditional non-intrusive operatorinference ones, the proposed approach alleviates the need of measuring theoriginal full-order model internal variables. It is thus applicable to a widerapplication range than standard intrusive and non-intrusive methods. Therationale is successfully applied on a large eddy simulation of a pollutantsdispersion case over an airport area involving multi-scale and multi-physicsdynamical phenomena. Despite the simplicity of the resulting low complexitymodel, the proposed approach shows satisfactory results to predict thepollutants plume pattern while being significantly faster to simulate.

Mixed interpolatory and inference non-intrusive reduced order modeling with application to pollutants dispersion