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.

翻译：本文件根据从复杂的模拟器中收集的输入-输出时间-时间-域数据,提出一种建设性的方法,用以推断一个在变化不定的动态模型中递减顺序线性、双线性或二次时间性时间,在变化不定的动态模型中产生基本现象;该方法基本上以线性动态系统和近似理论为基础;更具体地说,它依次涉及跨线性Pencil和Loewner框架,据知它非常多样,可扩缩至大型数据集,以及涉及原始数据和减少的内部变量的线性最低平方问题。关于侵入性方法,不需要事先了解操作者的情况。此外,与传统的非侵入性操作者推断方法相比,拟议方法减轻了测量原全线性模型内部变量的需要,因此适用于比标准侵入和非侵入性方法更广泛的应用范围。该原理成功地适用于机场地区涉及多尺度和多物理动态现象的污染物扩散案例的大规模模拟。尽管由此产生的低复杂性模型比较简单,但拟议方法显示快速的模拟结果,同时对磁质进行预测。