Dr. Kuan Li | Geomagnetism | Research Excellence Award
Dr. Kuan Li | Geomagnetism | Researcher at Sichuan University, China
Geomagnetism forms the core of the scientific identity, pioneering contributions, and international research trajectory of Dr. Kuan Li, a distinguished physicist whose work spans mathematical modeling, geodynamo theory, magnetohydrodynamics, data assimilation, and machine learning applications to complex geophysical systems. Throughout his career, Dr. Kuan Li has established himself as a leading innovator in the theoretical, numerical, and computational study of planetary magnetic fields, turbulent flows, and advanced dynamical processes that govern the evolution and variability of geomagnetic phenomena. Currently serving as Chief Scientist at Cloud-Stack Science and Technology LTD and as a researcher in the Institute of Atomic and Molecular Physics at Sichuan University, Dr. Kuan Li integrates high-performance computation, data-driven modeling, and advanced physics-based simulation to design next-generation scientific tools for geomagnetism and fluid dynamics. His prior professional appointments include serving as Assistant Professor at the State Key Laboratory of Lunar and Planetary Science and the Macau Institute of Space Technology and Application at Macau University of Science and Technology; Visiting Scholar at the State Key Laboratory of Lunar and Planetary Science in Zhuhai; REC Research Fellow in the Astrophysical and Geophysical Fluid Dynamics Group at the University of Leeds; and Senior Scientist (Oberassistent) and Postdoctoral Researcher in the Earth and Planetary Magnetism Group at ETH Zurich, where he also completed his PhD on numerical approaches to the geodynamo problem after earlier studies in theoretical physics and electrical engineering at the University of Oulu and undergraduate training in physics at Sichuan University. His research interests extend broadly across new mathematical methods for studying geomagnetism, statistical approaches for modeling turbulent fluid flows, theoretical and computational investigations of fluid dynamical and magnetohydrodynamical systems, machine-learning-driven prediction of nonlinear dynamics, and the development of sophisticated numerical schemes to advance geodynamo theory. Highly skilled in numerical analysis, data assimilation, optimal control theory, large-scale simulation, mathematical modeling, and machine-learning-based prediction, Dr. Kuan Li has contributed transformative insights into the optimal kinematic dynamo problem, variational data assimilation, instantaneous geostrophic flow reconstruction, and the dynamics of magnetostrophic regimes. His collaborations with leading international researchers have produced influential results in journals such as Journal of Computational Physics, Journal of Fluid Mechanics, Physical Review E, and Geophysical Journal International. Through his work, Dr. Kuan Li has shaped modern perspectives on how planetary magnetic fields are generated, sustained, and perturbed. His achievements have been further strengthened by awards, institutional recognitions, and invitations to contribute to major research collaborations in geomagnetism and dynamo theory. In conclusion, Dr. Kuan Li stands as a forward-thinking scientist whose contributions to geomagnetism, computational physics, and magnetohydrodynamics continue to push the boundaries of planetary science, offering mathematically rigorous, computationally powerful, and intellectually original frameworks that advance our understanding of Earth and planetary magnetic systems.
Featured Publications
Li, K., Livermore, P. W., & Jackson, A. (2010). An optimal Galerkin scheme to solve the kinematic dynamo eigenvalue problem in a full sphere. Journal of Computational Physics, 229(23), 8666–8683. Cited by 37.
Li, K., Jackson, A., & Livermore, P. W. (2011). Variational data assimilation for the initial-value dynamo problem. Physical Review E, 84(5). Cited by 32.
Chen, L., Herreman, W., Li, K., Livermore, P. W., Luo, J. W., & Jackson, A. (2018). The optimal kinematic dynamo driven by steady flows in a sphere. Journal of Fluid Mechanics, 839, 1–32. Cited by 30.
Li, K., Jackson, A., & Livermore, P. W. (2014). Variational data assimilation for a forced, inertia-free magnetohydrodynamic dynamo model. Geophysical Journal International, 199(3), 1662–1676. Cited by 26.
Li, K., Jackson, A., & Livermore, P. W. (2018). Taylor state dynamos found by optimal control: Axisymmetric examples. Journal of Fluid Mechanics, 853, 647–697. Cited by 20.
Pershin, A., Beaume, C., Li, K., & Tobias, S. M. (2023). Training a neural network to predict dynamics it has never seen. Physical Review E, 107(1), 014304. Cited by 12.
Hardy, C. M., Livermore, P. W., Niesen, J., Luo, J., & Li, K. (2018). Determination of the instantaneous geostrophic flow within the three-dimensional magnetostrophic regime. Proceedings of the Royal Society A. Cited by 10.