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Data-Centric Engineering is a peer-reviewed journal published by Cambridge University Press, covering Model Reduction and Neural Networks, Probabilistic and Robust Engineering Design, Structural Health Monitoring Techniques. Over its lifetime it has published 240 papers, which have received 2,564 citations.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Data-Centric Engineering 12 (2023) 45–58.
Formats any DOI in the closest standard style — Data-Centric Engineering has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Model Reduction and Neural Networks Probabilistic and Robust Engineering Design Structural Health Monitoring Techniques Infrastructure Maintenance and Monitoring Advanced Multi-Objective Optimization Algorithms |
| ISSN | 2632-6736 |
| Citation impact (2-yr) | 3.98 |
| h-index | 24 |
| i10-index | 62 |
| Article processing charge | $1,533 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | University of Cambridge |
| Journal website | www.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract The concept of a “digital twin” as a model for data-driven management and control of physical systems has emerged over the past decade in the domains of manufacturing, production, and operations. In the context of buildings and civil infrastructure, the notion of a digital twin remains ill-defined, with little or no consensus among researchers…
Abstract This paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and…
Abstract This paper presents the development process of a digital twin of a unique hydroponic underground farm in London, Growing Underground (GU). Growing 12x more per unit area than traditional greenhouse farming in the UK, the farm also consumes 4x more energy per unit area. Key to the ongoing operational success of this farm and…
Abstract The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method…
Abstract The Poisson equation is commonly encountered in engineering, for instance, in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field. In the present work, we propose a novel fully convolutional neural network (CNN) architecture to infer the solution of the…
