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CSAR lecture: Physics IS Enhancing Machine Learning

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If you have a question about this talk, please contact John Cook .

Moving away from accurate-but-wrong predictions for bridges, wind turbines… and the climate. Machine Learning algorithms are revolutionising many scientific fields by enabling the development of models from observations – so called data-driven. However, in many engineering applications, we usually have access to a limited amount of “informative” data – hindering the applicability data-driven approaches – but a great deal of physics understanding and domain knowledge! This opens up opportunities to combine physics and domain knowledge with data-driven approaches for guiding high-consequence decision making in engineering applications.

This seminar will give a brief non-technical introduction to Machine Learning and an overview of recent research work carried out within the Data, Vibration and Uncertainty Group (https://sites.google.com/view/dvugroup) focusing on developing Physics Enhanced Machine Learning (PEML) strategies in applied mechanics. It will showcase recent PEML methods developed for tackling challenges in wind turbines, bridges and structural joints, and ongoing efforts for investigating climate repair strategies.

Open to all. More details including a link for booking, here.

This talk is part of the Cambridge Society for the Application of Research (CSAR) series.

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