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A theoretical framework that supports automated construction of dynamic primemodels purely from experimental time series data has been invented anddeveloped, which can automatically generate (construct) data-driven models ofany time series data in seconds. This has resulted in the formulation andformalisation of new reverse engineering and dynamic methods for automatedsystems modelling of complex systems, including complex biological, financial,control, and artificial neural network systems. The systems/model theory behindthe invention has been formalised as a new, effective and robust systemidentification strategy complementary to process-based modelling. The proposeddynamic modelling and network inference solutions often involve tacklingextremely difficult parameter estimation challenges, inferring unknownunderlying network structures, and unsupervised formulation and construction ofsmart and intelligent ODE models of complex systems. In underdeterminedconditions, i.e., cases of dealing with how best to instantaneously and rapidlyconstruct data-consistent prime models of unknown (or well-studied) complexsystem from small-sized time series data, inference of unknown underlyingnetwork of interaction is more challenging. This article reports a robuststep-by-step mathematical and computational analysis of the entire prime modelconstruction process that determines a model from data in less than a minute.

Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!