Active learning for modeling and prediction of dynamical fluid processes

Abstract Accurate prediction of the flow rate curve of a stroke for reciprocating multiphase pumps often encounters several challenges in practice, including process nonlinearity, dynamical characteristics, and changing multiphase transportation conditions. To enhance the prediction performance, an active learning method is proposed to efficiently design informative training data. Some initial training data are first collected from experiments to construct several local Gaussian process regression (GPR) models. Additionally, with the GPR-based probabilistic information, a relative variance-based criterion is proposed to explore which regions the new data should be introduced into the GPR prediction model. Moreover, an evaluation criterion is designed to implement the active learning procedure efficiently. Consequently, without time-consuming experiments, a set of new representative training data are sequentially introduced into the GPR prediction model. Experimental results and comparative studies for dynamical flow rate prediction of a stroke are carried out to demonstrate the effectiveness of the proposed method.