Data-Centric Engineering is proud to sponsor the Institute of Physics (IOP) workshop on Physics Enhancing Machine Learning Workshop: Mechanics & Materials (the most recent edition of which took place on 27 November 2024). 

The event aims to share 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 is 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). 

Participants from the workshop are given the option of submitting a fully developed research paper to DCE, which undergoes the standard DCE peer review process. This special collection gathers together articles deriving from the workshops in 2022, 2023 and 2024. 

Editors: Alice Cicirello (University of Cambridge); Zack Xuereb Conti (The Alan Turing Institute).

Call for Papers: For more information on how to submit by the May 26, 2025 deadline, read here.

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