Data-Centric Engineering - an open access journal published by Cambridge University Press at the interface of data science and all areas of engineering - is delighted to partner with the Institute of Physics (IOP) workshop on Physics Enhancing Machine Learning, which takes place in London on 1-3 October 2025. 

We encourage participants in the workshop to develop their contributions into a full journal article to submit to DCE. Those that are accepted after peer review are added to an ongoing special collection page that is dedicated to the workshop. (We are also open to submissions from authors who do not attend the workshop but are working in this area). 

We welcome your contributions on advanced techniques and industrial applications showcasing recent progress, strengths and limitations of approaches integrating physics knowledge (first principles, domain knowledge, physics constraints, …) with Machine Learning (ML) in applied mechanics and materials. Particular interest will be given to contributions focusing on strategies including (but not limited to) those leveraging on observational biases (e.g. data augmentation), inductive biases (e.g. physical constraints), learning biases (e.g. inference/learning algorithm setup), and model form/discrepancy biases (e.g. equation terms describing a partially known physics-based model). 

Relevant topics include, but are not limited to: 

• (i) overcoming poor generalisation performance and physically inconsistent or implausible predictions; 
  (ii) providing explainable and interpretable inferences;  
• (iii) identifying incorrect data and/or physics biases;  
• (iv) validating modelling and forecasting;  
• (iv) quantifying different sources of uncertainty. 

Timetable

See the Institute of Physics workshop page for more details about the event: 

• Event takes place: 1-3 October 2025
• Submission to DCE: 1 June 2026 (earlier submission is welcomed and may lead to earlier publication)

Submission Guidelines

Please note the following key details, with more information available in the DCE Instructions for Authors:

Article types: DCE encourages the submission of original research papers, translational papers, systematic reviews and tutorial papers. 

Templates: DCE LaTeX and Word templates are available. Articles should be submitted through the DCE ScholarOne Manuscripts system. Alternatively, the CUP Data template in the authoring tool Overleaf can be used. Overleaf is particularly useful for co-authored papers - with collaborative features, versioning and a direct submission option into the DCE peer review system. 

Abstract and Impact Statement: 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)

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 (2024 Impact Factor: 2.8; 2024 Cite Score: 4.4) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ Open Access with support for unfunded authors thanks to the Lloyd's Register Foundation; any author can publish irrespective of funding or institution. 
✔ Promotes open sharing of data and code through Open Science Badges.

When submitting your contribution please select the ‘Physics Enhancing Machine Learning in Applied Solid Mechanics’ tag in the ‘Special Collection’  drop down menu. 

Please contact dce@cambridge.org with any queries about article preparation.

Open Access

Any author can publish on an open access basis in DCE if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles are covered through the Transformative Agreements that Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers open access publishing costs. Authors not affiliated with these agreements who have grants that budget for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived without question. DCE is supported by a grant from the Lloyd’s Register Foundation, which helps subsidise the publishing costs of unfunded authors.

Previous publications

You can read articles deriving from the 2022, 2023 and 2024 workshops in the Physics Enhancing Machine Learning special collection here.

Guest Editors

• Alice Cicirello (University of Cambridge)