Research summary

Our research focuses on the development, analysis, and application of high-precision and efficient numerical solvers and reduced-order modeling for problems arising in fluid dynamics. Topics include high-order accurate structure-preserving numerical methods for solving hyperbolic partial differential equations, and data-driven or machine-learning-based reduced-order modeling for parametrized time-dependent problems. The methods developed can be applied to real applications, such as magnetic confinement fusion in Tokamak, unmanned aerial vehicle navigation, etc.

We are a group led by Dr. Junming DUAN at the Chinese University of Hong Kong (Shenzhen). We are part of the School of Science and Engineering (SSE).

We are looking for motivated candidates at all levels, see Openings.

Research interests

Numerical methods:

• high-order accurate methods
• entropy stable methods
• physical-constraints-preserving methods
• adaptive moving mesh methods
• active flux methods

Reduced-order modeling:

• reduced basis method
• data-driven methods
• machine-learning-enhanced methods

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