Dr Yongxing Wang

Dr Yongxing Wang


I pursued my PhD in the field of fluid-structure interactions at the School of Computing, University of Leeds, from January 2015 to March 2018. During that time, I also worked as a part-time research assistant for Gillette P&G, modelling and simulations of shaving. This was followed by three Research Fellow positions in different departments within the university. I joined the School of Computing as a lecturer in 2022.

Prior to my doctoral studies, I worked as a software engineer at ECTec in Beijing from 2011 to 2014, where I developed finite element software (pFEPG). I taught high school math at YongLeDian High School in Beijing from 2010 to 2011. I also worked as a lecturer in mathematics at Hebei Agriculture University in BaoDing, China, from 2004 to 2007, teaching Calculus, Linear Algebra, Probability and Statistics, and Finite Element Analysis.

My academic and professional background is firmly rooted in Applied Mathematics and Computational Engineering, with experience gained over many years in both academia and industry. My work has encompassed various 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 specific interest in the design, implementation, and application of numerical methods for solving partial differential equations within the fields of fluid and solid mechanics. I am passionate about the modelling and simulation of fluid and solid dynamical systems, especially the complex interactions between them. This includes the implementation of large-scale simulations using parallel computing to solve problems, such as the aortic valve interacting with surrounding blood and tissues, or the simulation of biological swimmers moving through viscous fluids, which are the two main applications I am currently exploring.

Another area of interest for me is solving inverse problems related to the aforementioned mechanical and biological applications by formulating optimal control problems at the continuous level. This facilitates rigorous numerical analysis and the study of the coupling 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 the optimisation processes.

By joining Leeds WormLab, I am enthusiastic about comprehending the mechanism of C. elegans locomotion. This entails both forward and inverse modeling of C. elegans, incorporating data from laboratory experiments, using either a Cosserat rod or a 3D active fluid-structure interaction model. I am keen on gaining a more profound understanding of the muscle force distributions within the worm, along with its neural control mechanisms operating throughout its body. I am also interesed in extending the foundational principles and insights acquired from the study of C. elegans to other biological organisms or even engineering systems, such as the locomotion of sperm cells, microbots, or bio-inspired engineering designs.

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>


  • 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

  • SIAM
  • FHEA

Student education

Numerical Computation

Research groups and institutes

  • Computational Science and Engineering
<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>