LIFD Early Career Researcher Spotlight: Oluwaseun Coker

Thesis title: Learning the Evolution of Time-dependent Partial Differential Equations

School/ Faculty:  CDT in Fluid Dynamics, School of Computing

Supervisors:  Prof. Peter Jimack (Uni of Leeds), Dr. Amirul Khan (Uni of Leeds) and Dr. He Wang ( UCL)

Tell us a bit about yourself:

I'm a final-year PhD candidate with a background in fluid dynamics, numerical methods and machine learning methods. I earned my Bachelor's and Master's degrees in Aerospace Engineering and Aerodynamics & Computation from the University of Southampton, where my research focused on simulating rotating turbulence and stall cell behaviour. I've also gained practical experience as an engineer in the engine industry and as a research assistant studying multiphase flows. I'm passionate about problem-solving and eager to apply my skills to new challenges.

When I'm not immersed in research, I enjoy catching up with friends over coffee or perfecting my home barista skills with my trusty Coco Bean. And while I love football, being a Manchester United supporter right now requires a bit of resilience!

What is your research about?

My research explores the application of machine learning models for predicting solutions to time-dependent partial differential equations. I'm particularly interested in equations related to fluid dynamics that change over time.  Traditional numerical methods are quite expensive for complex and large problems; however, machine learning can offer a promising alternative to alleviate the computational cost of numerical simulations, especially in design optimisation tasks. Using machine learning models for predicting the evolution of these equations is challenging, therefore, my research focuses on developing better models and training strategies to improve their accuracy and reliability.

What did you wish you knew before starting a PhD?

If I could give my past self one crucial piece of advice before embarking on this PhD journey, it would be this: prepare to be surprised, and embrace the unexpected. I came in with a collection of what I believed were solid, exciting research ideas. However, I quickly learned that the scientific process is rarely linear. Not every promising hypothesis leads to the expected outcome, and that's not a failure, it's a fundamental part of discovery.

The reality is that your research plan will likely evolve, sometimes drastically, as you gather results. You might find your initial approach needs significant adjustments, or even a complete rethinking. This isn't a sign of weakness or a lack of good ideas. Rather, it's a testament to the dynamic nature of research.

Adaptability is key. Learning to pivot, to reassess, and to adjust my plans based on the evidence has been essential. Don't cling too tightly to your initial vision. Be open to the unexpected turns, learn from every experiment, regardless of its outcome, and remember that every step, even the detours, contributes to your growth as a researcher. Embrace the fluidity (no pun intended), and you'll find the PhD experience far more rewarding.

What are your plans for the future

I'm eager to leverage my acquired knowledge and skills in a challenging research environment that fosters continuous learning. I'm considering both industry R&D and academic postdoc positions, and I'm open to exploring diverse opportunities within those fields.