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 for a second year with the Institute of Physics (IOP) workshop on Physics Enhancing Machine Learning in Applied Solid Mechanics (which took place in London, on 20 November 2023). 

Articles developed through the workshop will be published in a dedicated, specially curated collection in DCE after peer review. We encourage workshop participants but also those who did not attend the workshop to contribute to the DCE special collection. 

We welcome contributions on advanced techniques and industrial applications showcasing recent progress, strengths and limitations of using physics knowledge to enhance Machine Learning strategies in applied solid mechanics. 

Particular interest is given to contributions focusing on how physics domain knowledge and the availability of a causal physics-based model enable one to move from accurate-but-wrong predictions, to explainable and interpretable inferences fully exploiting machine learning techniques in applied solid mechanics.

Relevant topics include, but are not limited to: 

• Probabilistic Model updating, 
• Virtual Sensing, 
• Structural Health Monitoring, 
• Identification of system parameters and non-linear relationships, 
• Uncertainty Quantification, 
• Reduced Order Modelling of Nonlinear problems, 
• Physics-informed Neural Networks, 
• Reinforcement Learning,
• Transfer Learning.

Timetable

We encouraged interested parties to submit manuscripts as soon as they are ready., with a final deadline of 31 July 2024.

Articles go through the standard peer review process. Articles will be published as soon as possible after acceptance, in the interest of allowing authors to disseminate their work without unnecessary delay. At a later point they will be added to a curated page for the collection with an editorial reflecting on the insights of the articles will be published at a later date.

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)

Open Materials

Authors are encouraged make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. We recognise this may not be possible in all circumstances. See the DCE Research Transparency policy for more details. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.

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.

Editors

• Alice Cicirello (University of Cambridge)
• Zack Xuereb Conti (The Alan Turing Institute)