Biography
Kin Fung Chan joined the University of Oxford as a DPhil Candidate in September 2020 and has since completed his thesis. He previously obtained a MEng in Mechanical Engineering from the University of Sheffield, with a year at the Hong Kong University of Science and Technology (HKUST). Kin has industrial experience with placements at AMD, BAE Systems, Rolls Royce and E.ON Climate and Renewables.
Currently, Kin is carrying out research in the field of Computational Mechanics, focusing on the numerical simulations of highly dynamic mechanical systems. His PhD successfully investigated the reduction in the computational expense of finite element methods with multi-time step integration methods and mapping schemes for non-matching meshes. His work as a Postdoctoral Researcher involves diverse computational approaches, ranging from the development of physics-informed neural networks to the application of finite element modelling in shock physics experiments.
His research previously required collaboration with corresponding academic research teams in the Advanced Simulation and Modelling of Engineering Systems (ASiMoV) consortium, including researchers from Rolls-Royce plc. Now, he works within the AMPLIFI Prosperity Partnership, funded by First Light Fusion.
Research Interests
- Finite Element Methods for Deformable Solids
- Computational Mechanics
- Numerical Methods in Engineering
Research Groups
Current Projects
- Spurious Wave Attenuation for Non-uniform grids
- Numerical Modelling of Richtmyer–Meshkov instabilities
- Multi-Time Stepping Algorithms for Explicit Finite Elements
Related Academics
Publications
- https://doi.org/10.1002/nme.7638 -Chan, Kin Fung, et al. "A Multi‐Time Stepping Algorithm for the Modelling of Heterogeneous Structures With Explicit Time Integration." International Journal for Numerical Methods in Engineering 126.1 (2025): e7638.
- https://doi.org/10.3390/ma18051080 Chan, Kin Fung, et al. "Temporal and Spatial Coupling Methods for the Efficient Modelling of Dynamic Solids." Materials 18.5 (2025): 1080.
Awards and Prizes
Best Presentation at Rolls Royce Doctorate Conference 2024 | Derby, UK
