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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.