Data-Centric Engineering - an open-access journal published by Cambridge University Press - is delighted to be partnering with the EuroMech/ERCOFTAC joint conference, which brings together communities from the Fluid Mechanics, Applied Mathematics, and Machine Learning in London on 2nd - 4th April 2025.
This conference will cover topics including:
- Physics-aware machine learning;
- Data assimilation;
- Model discovery;
- Reduced-order modelling;
- Scientific machine learning;
- Digital twins;
- Turbulence modelling;
- Flow control; and more!
Selected presentations relevant to the DCE scope will be invited to submit to the journal in order to form a special dedicated to Data-Driven Fluid Mechanics.
Timetable
See the conference website for details about the event:
- Workshop takes place 2-4 April 2025
- Submission to DCE: 3 October 2025 (earlier submission is welcomed and may lead to earlier publication)
Why Submit to DCE?
✔ A venue dedicated to the potential of data science for all areas of engineering.
✔ Welcoming research and translational articles from authors, whether they are based in academia or industry.
✔ Well-cited (2023 Impact Factor: 2.4; 2022 CiteScore: 5.6) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ #OpenAccess with support for unfunded authors thanks to the Lloyd's Register Foundation - no hard requirement to pay an article processing charge (APC).
✔ Promotes open sharing of data and code through Open Science Badges.
How to Submit
Key considerations for submitting are below, with full details available in the DCE Instructions for Authors.
Article types
DCE encourages the submission of:
- Research articles using data science methods and models for improving the reliability, resilience, safety, efficiency and usability of engineered systems.
- Translational papers demonstrating the downstream benefits of data-intensive engineering - and the underlying data science principles, techniques and technologies - to wider society, economy, environment, health and way of life. For some more detailed instructions, see this guide to translational papers. (Typically 6,000 words or less).
- Data papers that describe in a structured way, with a narrative and accompanying metadata, important and re-usable data sets in open repositories with potential for re-use in engineering research and practice. These papers promote data transparency and data re-use.
- Survey papers providing a detailed, balanced and authoritative current account of the existing literature concerning data-intensive methods in a particular facet of engineering sciences.
- Tutorial reviews providing an introduction and overview of an important topic of relevance to the journal readership. The topic should be of relevance to both students and researchers who are new to the field as well as experts and provide a good introduction to the development of a subject, its current state and indications of future directions the field is expected to take
- Position papers providing an overview of an important issue for this emerging field. (Typically 6,000 words or less).
Templates
Authors have the option but are not required to use the following templates:
- DCE LaTeX template files
- Overleaf (a LaTeX-based collaborative authoring tool; read about benefits of this tool)
- DCE Word template
Note that authors should provide both an abstract that summarises the paper (250 words or less) and beneath it an impact statement (120 words describing the significance of the findings in language that can be understood by a wide audience). Competing interest, funding and data availability statements should be provided at the end of the main text above the references (see disclosure statements).
Articles should be submitted through the DCE ScholarOne Manuscripts system using the special collection tag 'Data-Driven Fluid Mechanics' when prompted, but note that if you use the Overleaf tool you can submit directly into the system without having to reupload files.
Guest Editors
- Prof Luca Magri, Imperial College London - DCE Editor
- Prof Aaron Towne, University of Michigan
- Prof Gianluca Iaccarino, Stanford University
- Dr Andrea Novoa, Imperial College London
- Prof Taraneh Sayadi, CNAM
- Dr Anh Khoa Doan, TU Delft