Method and system for accelerating self-learning using meta learning in industrial process domain

Abstract

Large training times incurred during the process of self-learning of ML models in digital twins are debilitating and can adversely affect the functioning of industrial plants. Embodiments of the present disclosure provide a method and system for accelerated self-learning using application agnostic meta learner trained using optimal set of meta features selected from classification meta features, regression meta features, and domain meta features based on a domain-meta-feature-taxonomy created for a plurality of industrial plants across a plurality of domains. Optimal feature selection is enabled using ML, DL that provides static feature selection, while Q-learning based approach is disclosed that enables dynamic feature selection. Q-learning based approach has two implementations, static and dynamic reward. In the dynamic reward based approach the reward of choosing an action depends on current state (selected meta features), and reward for selecting the next meta feature, depends on the meta features that have already been selected.