Structural (Aero-) Dynamics Using Gaussian Processes

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• Igor Kavrakov, University of Cambridge
• Friday 18 October 2024, 15:00-16:00
• CivEng Seminar Room (1-33) (Civil Engineering Building).

If you have a question about this talk, please contact Sam Cocking.

Advances in machine learning and sensors have improved how we model and monitor structures, yet challenges remain in extracting meaningful information from high-dimensional, noisy data to create interpretable low-order models. This talk presents data-centric methodologies that combine Gaussian Processes (GPs) as a statistical regression tool with low-order models for probabilistic inference of structural behaviour. These approaches are designed to address problems in structural (aero-)elasticity, including modelling nonlinear aeroelastic forces acting on bluff bodies, inferring the behaviour of elastic beams and plates based on heterogeneous data, and the inverse reconstruction of dynamic loads. Additionally, a novel Bayesian method utilising structured variational inference is introduced for parameter and state estimation of dynamical systems. Potential applications of this method include reverse engineering low-order dynamical systems, such as limit cycle attractors in vortex-induced vibrations. In closing, and somewhat loosely related to the main topics, a probabilistic structural analysis of a reconstruction of the Lefkandi-Toumba Building, an ancient Greek longhouse from the Greek Dark Ages, will be presented.

This talk is part of the Engineering Department Structures Research Seminars series.

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