Abstract
To achieve its full predictive potential, a digital twin must consistently and accurately reflect its physical counterpart throughout its operational lifetime.To this end, the inverse mapping parameter updating method enables physically interpretable parameter values to be updated, in real-time, for a wide range of (nonlinear) dynamical models using features extracted from measured response data. This paper proposes to extend this method by employing a probabilistic Bayesian neural network, which is trained offline using simulated data, to infer, again in real-time, probability distributions for the updating parameter values instead of (traditionally obtained) point estimates. As a result, the user obtains a quantification of the (un)certainty, providing insight into the degree of trust to be placed in the updated parameter values, which supports the decision-making process for which the digital twin is used. Additionally, it is proposed to include so-called ‘input parameters’ (that characterize the specific settings on the physical setup) as inputs to the neural network to allow for a broader applicability of the updating method. To validate the proposed methodology, it is applied, using both simulated and real-world measurements, to a medical mechanical ventilation system, in which information about uncertainty in the inferred parameter values is important. Parameter values of this system and their uncertainties are shown to be inferred with sufficient accuracy.
Original language | English |
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Journal | Nonlinear Dynamics |
Volume | XX |
Issue number | X |
Early online date | 23 Nov 2024 |
DOIs | |
Publication status | E-pub ahead of print - 23 Nov 2024 |
Bibliographical note
Publisher Copyright:© The Author(s) 2024.
Funding
This work was (mainly) financed by the Dutch Research Council (NWO) as part of the Digital Twin project (subproject 2.1) with number P18-03 in the research programme Perspectief.
Funders | Funder number |
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Nederlandse Organisatie voor Wetenschappelijk Onderzoek | P18-03 |
Keywords
- model updating
- parameter estimation
- Digital Twin
- probabilistic Bayesian neural networks
- uncertainty quantification
- experimental industrial use case
- Parameter estimation
- Digital twin
- Experimental industrial use case
- Uncertainty quantification
- Model updating
- Probabilistic Bayesian neural networks