Physics Enhancing Machine Learning in Applied Solid Mechanics
This workshop sponsored by the Institute of Physics Applied Solid Mechanics group 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 expertise 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. Registration is free, but only 30 places for in person attendance are available – 10 of which are reserved to early career researchers. It will be possible to virtually attend without presenting.