Digital Twin : A Dynamic System and Computing Perspective (Hardcover)

Chapter 1 Introduction and Background1.1 Introduction1.2 Modeling and Simulation1.3 Sensors and Actuators1.4 Signal Processing1.5 Estimation Algorithms1.6 Industry 4.01.7 Applications Chapter 2 Computing and Digital Twin2.1 Digital Twin Use cases and the Internet of things (IOT)2.2 Edge Computing2.3 Telecom and 5G2.4 Cloud2.5 Big Data2.6 Google Tensorow2.7 Blockchain and digital twin Chapter 3 Dynamic Systems3.1 Single-degree-of-freedom undamped systems3.2 Single-degree-of-freedom viscously damped systems3.3 Multiple-degree-of-freedom undamped systems3.4 Proportionally damped systems3.5 Non-proportionally damped systems3.6 Summary Chapter 4 Stochastic Analysis4.1 Probability theory4.2 Reliability4.3 Simulation methods in UQ and reliability4.4 Robustness Chapter 5 Digital Twin of Dynamic Systems5.1 Dynamic model of the digital twin5.2 Digital twin via sti ness evolution5.3 Digital twin via mass evolution5.4 Digital twin via mass and sti ness evolution5.5 Discussions5.6 Summary Chapter 6 Machine learning and Surrogate Models6.1 Analysis of Variance Decomposition6.2 Polynomial Chaos Expansion6.3 Support Vector Machines6.4 Neural Networks6.5 Gaussian Process6.6 Hybrid polynomial correlated function expansion Chapter 7 Surrogate based digital twin of dynamic system7.1 The dynamic model of the digital twin7.2 Overview of Gaussian process emulators7.3 Gaussian process based digital twin7.4 Discussion7.5 Summary Chapter 8 Digital Twin at Multiple Time Scales8.1 The problem statement8.2 Digital twin for multi-timescale dynamical systems8.3 Illustration of the proposed framework8.4 Summary Chapter 9 Digital twin of nonlinear MDOF systems9.1 Physics based nominal model9.2 Bayesian ltering algorithm9.3 Supervised machine learning algorithm9.4 High delity predictive model9.5 Examples