About the project
Can AI learn to understand, not just predict, complex engineering dynamical systems? In this PhD, you will combine deep learning with the mathematics of nonlinear dynamics to uncover the hidden structure behind vibrations, flutter and instabilities in aerospace systems, creating fast, interpretable models for safer, lighter and net-zero flight.
Modern aerospace systems, from aero-engine structures to flexible wings and electrified propulsion, are becoming lighter, smarter and more strongly coupled across physics. As a result, they show complex nonlinear dynamics: flutter and other limit cycle oscillations, multiple coexisting stable states, internal resonances and sudden bifurcations. High-fidelity simulations of these behaviours are too slow for design and control, while standard machine learning models act as black boxes that are hard to trust or interpret.
This project will develop interpretable, data-driven reduced order models by connecting deep learning of latent dynamics with invariant manifold theory, which shows that complex systems are often governed by hidden low-dimensional structures. The research works in both directions:
- theory to learning: embed invariant manifold and normal form theory into deep learning architectures, giving latent spaces physical meaning
- learning to theory: use deep learning to discover invariant manifolds and nonlinear dynamics directly from sparse, high-dimensional data
- uncertainty quantification: make predictions robust to noise, sparse sensing and real-world variability
Expected outcomes include a new modelling framework, validated on nonlinear aero-engine structures, aeroelastic systems and electro-mechanical systems, and released as open-source software. The work will support faster, more reliable design of safer, lighter and more efficient aircraft for net-zero aviation.
You will join the Computational Engineering and Design group, with access to the University's high-performance computing facilities, a tailored doctoral training programme and opportunities to present at international conferences.
You will receive training in scientific machine learning, nonlinear dynamics and reduced order modelling through one-to-one supervision, group meetings and research seminars. You will gain hands-on experience with high-performance computing on the University’s Iridis supercomputer.
The University’s Doctoral College provides a researcher development programme covering research skills, academic writing, presentation and career development. You will also have opportunities to present at international conferences, publish in leading journals, and gain teaching experience as a demonstrator.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
