Modelling and Estimation in Digital Twins
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 661
Special Issue Editors
Interests: inverse problems; data sketching; randomised computing; digital twins
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Digital twinning is the coupling between a dynamic system (physical asset) and its computerised model (digital asset). The scope of this is to allow for prediction and optimisation of operations via the digital asset over the lifetime of the physical one. This, in turn, requires that the computerised model is promptly calibrated so that its response matches as close as possible the behaviour of the physical asset via a continuous cycle of sensor data assimilation and model prediction, tracking the temporal evolution of the physical system. This framework is rooted in inverse problems and mathematical modelling under uncertainty, and finds applications in automation of manufacturing processes, structural health monitoring and biomedical signal and image processing, among other areas. Of particular importance to the digital twins context is the computational efficiency of the algorithms involved and their scalability in increasing dimensions, as model estimation and prediction tasks must be available in near real-time to allow for timely decisions and controls.
This Special Issue aims to bring together articles discussing recent algorithmic advances in mathematical modelling and inverse problems of high-dimensional and complex systems. Articles on topics in real-time simulation, model order reduction, online learning, scalable uncertainty quantification and data-driven models are particularly welcome.
Dr. Nicholas Polydorides
Dr. Dimitris Kamilis
Guest Editors
Manuscript Submission Information
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Keywords
- real-time simulation/estimation
- model order reduction
- uncertainty quantification
- online learning
