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Physics-Enhanced Machine Learning (PEML, also referred to as hybrid, grey-box modelling or scientific machine learning)  focuses on the integration of information that is typically extracted from real-world data, physics-based models and domain and expert knowledge. PEML is a natural evolution of Machine Learning (ML) tackling issues such (i) poor generalization performance and physically inconsistent/implausible predictions; (ii) inability of accounting for and quantifying the different sources/types of uncertainties; (iii) inability of providing explainable and interpretable inferences.  PEML strategies constrain the space of admissible solutions and therefore enable robust and interpretable models even with limited data. These approaches can allow for real-time model updating, improved forecasting, and the ability to infer system behavior in the presence of sparse or noisy measurements. PEML strategies are critical for (i) informing the physics describing the underlying dynamical system to be able to analyze, control and predict the wide range of behaviours of the real-world system; (ii) providing fast and accurate solutions of hybrid physics-data models, including governing equations, reduced order models, prediction, forecasting and simulation models (iii) identify experiments that need to be carried out.

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). 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).