University of Cambridge > > Engineering - Dynamics and Vibration Tea Time Talks > A spectrum of physics-informed machine learning approaches for problems in structural dynamics

A spectrum of physics-informed machine learning approaches for problems in structural dynamics

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As monitoring data from our critical systems and structures become more abundant, engineers (should) naturally wish to benefit from the learning available from them. Indeed, many elements of structural assessment and, in particular, those relying on a dynamic signature, are now evolving to take advantage of this, leading to the creation and adoption of a wealth of data-driven approaches. The use of machine learning in structural health monitoring, for example, is common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit.

A significant challenge here, that is not often acknowledged, however, is that we commonly lack representative data from across the range of environmental and operational conditions structures will undergo, limiting the usability of an entirely data-based approach.

This talk will present a number of ways of incorporating the physical insight an engineer will often have of the structure they are attempting to model or assess into a machine learning approach through a Gaussian process regression framework. The talk will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability for structural assessment and system identification tasks. A particular strength of the approaches demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes, increasing applicability in light of the aforementioned challenge.

This talk is part of the Engineering - Dynamics and Vibration Tea Time Talks series.

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