Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Abstract

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.