• Special issue collection on DCE: deadline:  running again!

Data-Centric Engineering - an open access journal published by Cambridge University Press at the interface of data science and all areas of engineering - is pleased to be partnering with the Institute of Physics (IOP) workshop on Physics Enhancing Machine Learning in Applied Solid Mechanics . 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.

Editors:

• Alice Cicirello (University of Cambridge)

• Zack Xuereb Conti (The Alan Turing Institute)