Bayesian system identification in nonlinear engineering dynamics

Details

Structural/engineering dynamics holds the key to designing safer, lighter and greener structures for the future. However, a grand challenge facing the discipline is that many structures are nonlinear. This presents a major problem as the mathematics that we usually rely on have almost always been created with linear systems in mind. The way around this issue in the past has been to exploit approximate or computational solutions; however, this can be unsatisfactory in that it lacks in valuable physical insight. This insight is often crucial in changing partial mathematical solutions into practical/applicable engineering solutions. Furthermore, it can be difficult to actually establish the equations of motion of nonlinear systems; this can be accomplished using system identification – the subject of this project.

This PhD project is funded as part of Professor Keith Worden’s EPSRC Open Fellowship on New Ways Forward in Nonlinear Structural Dynamics. It will support the main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods. The students will be part of the University of Sheffield Dynamics Research Group – one of the largest dedicated structural dynamics groups in the world. The students will also have the opportunity to carry out experimental validation in some of the best facilities available.

To some extent, the project can be tailored to the specific interests and skills of the applicants, although it will depend on a degree of mathematical sophistication, so applicants should have an appropriate degree in engineering, mathematics, physics or machine learning; they must also have a drive to carry out research in dynamics.

Queries and CVs/covering letters from home students can be directed to the primary supervisor, Professor Keith Worden at k.worden@sheffield.ac.uk

Start date: September 2025

How To Apply

Applications should be made at: PhD study | MAC | The University of Sheffield

Applications should include:

-              Personal statement

-              Curriculum Vitae

-              Two reference letters

-              Degree transcripts to date