After the success of the first workshop in 2022, the Institute of Physics Applied Mechanics group is sponsoring another exciting workshop on Physics Enhancing Machine Learning in Applied Mechanics. We are also excited to acknowledge the generous support from the Data Centric Engineering (https://www.cambridge.org/core/journals/data-centric-engineering)  and Siemens (https://plm.sw.siemens.com/en-US/simcenter/)! 

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. 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, strategies for: (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.

Registration is free, but only 60 places for in person attendance are available – 20 of which are reserved to early career researchers. It will be possible to attend virtually without presenting.

Moreover, this year, we are happy to announce the Early Career DCE travel award! We sincerely hope this will help us further removing barriers preventing some early careers to attend such events.

Chair of the workshop: Alice Cicirello (University of Cambridge) and co-opted member of the IOP Applied Mechanics Group.

Key Dates:

Abstract submission: 23 October 2023
Notification of abstract acceptance: 30 October 2023
Registration deadline: 14 November 2023

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