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

by   Shailesh Garg, et al.

The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive 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 approach proposed in this paper strategically decouples the problem into two time-scales – (a) a fast time-scale governing the system dynamics and (b) a slow time-scale governing the degradation in the system. The proposed digital twin has four components - (a) a physics-based nominal model (low-fidelity), (b) a Bayesian filtering algorithm a (c) a supervised machine learning algorithm and (d) a high-fidelity model for predicting future responses. The physics-based nominal model combined with Bayesian filtering is used combined parameter state estimation and the supervised machine learning algorithm is used for learning the temporal evolution of the parameters. While the proposed framework can be used with any choice of Bayesian filtering and machine learning algorithm, we propose to use unscented Kalman filter and Gaussian process. Performance of the proposed approach is illustrated using two examples. Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.


page 1

page 2

page 3

page 4


Machine learning based digital twin for dynamical systems with multiple time-scales

Digital twin technology has a huge potential for widespread applications...

Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems

For real-life nonlinear systems, the exact form of nonlinearity is often...

Learning governing physics from output only measurements

Extracting governing physics from data is a key challenge in many areas ...

High-Fidelity State-of-Charge Estimation of Li-Ion Batteries Using Machine Learning

This paper proposes a way to augment the existing machine learning algor...

A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing

The growing interest in creating a parametric representation of liquid s...

The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling

The concept of a digital twin has exploded in popularity over the past d...

Multi-fidelity wavelet neural operator with application to uncertainty quantification

Operator learning frameworks, because of their ability to learn nonlinea...