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 developed finite element software (pFEPG). Additionally, I was a mathematics lecturer at Hebei Agricultural University in Baoding, China, from 2004 to 2007, where I taught courses in Calculus, Linear Algebra, Probability and Statistics, and Finite Element Analysis.
My academic and professional background is firmly rooted in Applied Mathematics and Computational Engineering, enriched by extensive experience in both academia and industry. My research interests encompass a variety of topics, including fluid-structure interactions, optimal control for fluid dynamical systems, Gaussian processes for machine learning, and surrogate optimisation.
Research interests
My primary research focus is scientific computation, with a particular interest in the design, implementation, and application of numerical methods for solving partial differential equations in the fields of fluid and solid mechanics. I am passionate about the modelling and simulation of fluid and solid dynamical systems, particularly their complex interactions. This includes conducting large-scale simulations using parallel computing to solve problems such as the interaction between the aortic valve and the surrounding blood and tissues, or the simulation of biological swimmers moving through viscous fluids—two key applications I am currently exploring.
Another area of interest is solving inverse problems related to the aforementioned mechanical and biological applications by formulating optimal control and shape optimisation problems at the continuous level. This approach enables rigorous numerical analysis and the study of the coupling between fluids and solids, as well as between the primal and adjoint systems. Additionally, I employ data-driven methods such as Gaussian process regression or neural networks to construct surrogate models for optimisation processes.
Through my involvement with Leeds WormLab, I am enthusiastic about investigating the mechanisms of C. elegans locomotion. This involves both forward and inverse modelling of C. elegans, incorporating data from laboratory experiments, using either a Cosserat rod model or a 3D active fluid-structure interaction model. I aim to gain a deeper understanding of the muscle force distributions within the worm, as well as its neural control mechanisms. I am also interested in extending the foundational principles and insights gained from the study of C. elegans to other biological organisms or engineering systems, such as the locomotion of sperm cells, microbots, or bio-inspired engineering designs.
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