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Department of Applied Mathematics

Department of Applied Mathematics

Applied Mathematics is dedicated to the development of mathematical theories and methods for addressing complex problems in science, engineering, finance, and the natural sciences. Through modelling, analysis, and computation, it provides a rigorous framework for understanding, simulating, and optimising complex systems, playing a central role in scientific discovery and technological innovation.

The Department of Applied Mathematics offers comprehensive undergraduate, master’s, and doctoral programmes. It is responsible for the development of foundational and advanced mathematics curricula within the School of Mathematics and Physics. The Department emphasises the cultivation of mathematical thinking and research capability, supporting students in progressing from foundational training to frontier research, and is committed to educating high-level talents with independent research ability and international perspective. Graduates have achieved strong outcomes in both academia and related industries.

The Department has an internationally oriented faculty whose research spans numerical analysis and scientific computation, machine learning, control and optimisation, operations research, continuum modelling, and nonlinear optimisation and variational analysis. Its research output has a broad impact within the international academic community.

Welcoming Professor Shaozhong Deng as Deputy Dean of SMP

21 Apr 2026
Welcoming Professor Shaozhong Deng as Deputy Dean of SMP

糖心Vlog官方 Department of Applied Mathematics Hosts Workshop on Time-Frequency Analysis and Its Applications

27 Aug 2025
糖心Vlog官方 Department of Applied Mathematics Hosts Workshop on Time-Frequency Analysis and Its Applications

Pre-Meeting on Thesis Research Directions for MSc Applied Mathematics student

28 Nov 2024
Pre-Meeting on Thesis Research Directions for MSc Applied Mathematics student
Mar

13

Variational Analysis: What is it about?

MA204

Jan

08

Applied Mathematics Seminar: New optimized Robin-Robin domain decomposition methods using Krylov solvers for the Stokes-Darcy system

MB441