Dr Yongxing Wang
- Position: Lecturer
- Areas of expertise: finite element methods; computational fluid dynamics; fluid-structure interaction; partial differential equations; optimal control; Gaussian process for machine learning; surrogate-based optimisation.
- Email: Y.Wang3@leeds.ac.uk
- Phone: +44(0)113 343 4874
- Location: 3.36, Sir William Henry Bragg Building
- Website: My Personal Site | Googlescholar | Researchgate | ORCID
Profile
I earned my PhD in fluid–structure Interactions from the School of Computing at the University of Leeds, where I studied from January 2015 to March 2018. During my doctoral studies, I also worked part-time as a Research Assistant for Gillette P&G, focusing on modelling and simulations related to shaving. Following my PhD, I held three Research Fellow positions in different departments at the university before joining the School of Computing as a Lecturer in 2022.
Prior to pursuing my doctoral studies, I worked as a software engineer at ECTec in Beijing from 2010 to 2014, where I focused on developing finite element software, pFEPG. Additionally, I was a mathematics lecturer at Hebei Agricultural University in Baoding, China, from 2004 to 2007, teaching Calculus, Linear Algebra, Probability and Statistics.
My academic and professional background is firmly rooted in Applied Mathematics and Computational Engineering, complemented by extensive experience in both academia and industry. My research interests encompass a range of topics, including fluid-structure interactions, optimal control of fluid dynamical systems, Gaussian processes for machine learning, and surrogate optimisation.
Research interests
My research interests broadly lie in the areas of scientific computation, differential equations, and mathematical physics. More specific topics include numerical methods for fluid-structure interactions, parallel implementation and preconditioning for large-scale simulations, modelling of biological locomotion, shape optimisation, adjoint-based optimal control, geometric integrators, exactly energy-conserving methods, Gaussian process regression and neural networks for data-driven optimisation, fluids on surfaces, and Navier–Stokes equations coupled with Einstein’s field equations.
Qualifications
- PhD in Computational Fluid Dynamics, University of Leeds, Jan.2015 - Jun.2018
- MSc in Computational Mathematics, Suzhou University, Sep.2007 - Sep.2010
- BSc in Information and Computational Science, Yanshan University, Sep.2000 - Sep.2004
Professional memberships
- AMIMA
- FHEA
- SIAM
Student education
Numerical Computation, Calculus, Probability
Research groups and institutes
- Computational Science and Engineering