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This workshop is sponsored by the Institute of Physics Applied Solid Mechanics group and welcomes 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 will be 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. 

Early Career Researcher Prize: Are you an early career researcher (Master/PhD student or Research Assistant/Associate) working in Physics-enhancing Machine Learning in Solid Mechanics? When submitting your abstract, please indicate that you would like to be considered for the prize! You might be the person selected for opening the technical session 2 or technical session 3!  

Chair of the workshop: Alice Cicirello (TU Delft) - co-opted member of the IOP Applied Solid Mechanics Group

Key Dates:

Abstract submission deadline3 November 2022
Registration deadline:28 November 2022







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