Enhanced multi-fidelity modelling for digital twin and uncertainty
  quantification
AS Desai Navaneeth N S Adhikari S Chakraborty
The increasing significance of digital twin technology across engineering and
industrial domains, such as aerospace, infrastructure, and automotive, is
undeniable. However, the lack of detailed application-specific information
poses challenges to its seamless implementation in practical systems.
Data-driven models play a crucial role in digital twins, enabling real-time
updates and predictions by leveraging data and computational models.
Nonetheless, the fidelity of available data and the scarcity of accurate sensor
data often hinder the efficient learning of surrogate models, which serve as
the connection between physical systems and digital twin models. To address
this challenge, we propose a novel framework that begins by developing a robust
multi-fidelity surrogate model, subsequently applied for tracking digital twin
systems. Our framework integrates polynomial correlated function expansion
(PCFE) with the Gaussian process (GP) to create an effective surrogate model
called H-PCFE. Going a step further, we introduce deep-HPCFE, a cascading
arrangement of models with different fidelities, utilizing nonlinear
auto-regression schemes. These auto-regressive schemes effectively address the
issue of erroneous predictions from low-fidelity models by incorporating
space-dependent cross-correlations among the models. To validate the efficacy
of the multi-fidelity framework, we first assess its performance in uncertainty
quantification using benchmark numerical examples. Subsequently, we demonstrate
its applicability in the context of digital twin systems.