Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system

The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and auto -motive sector. However, practical implementation of this technology is not at an expected speed, specifically because of lack of application-specific details. In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems. The proposed digital twin has four modules - (a) a physics-based nominal model, (b) a data collection module, (c) algorithm for real-time update of the digital twin and (d) module for predicting future state. The modules for real-time update and prediction are based on the so-called gray-box modeling approach, and utilizes both physics based and data driven frameworks; this enables the proposed digital twin to generalize and predict future responses. The gray box modeling framework used within the digital twin is developed by coupling Bayesian filtering and machine learning algorithm. Although, the proposed digital twin can be used with any machine learning regression algorithm, we have used Gaussian process in this study. Performance of the proposed approach is illustrated using two examples. Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.