The Continuum Modelling and Analysis Group studies mathematical models for complex dynamical systems arising in science and engineering. Our main focus is on nonlinear optimisation and variational analysis, particularly in settings involving nonsmooth and nonconvex phenomena. Current research directions include calculus of variations, optimal control, and deterministic as well as stochastic differential equations, together with both qualitative and quantitative aspects of dynamical systems. In addition, the group pursues research in fluid mechanics, applied harmonic and time-frequency analysis, as well as applications in signal processing, stochastic analysis, and numerical methods for differential equations.
The Control, Optimisation, and Operational Research Group specialises in linear and nonlinear dynamics, network optimisation, applied combinatorics, and dynamic optimisation. Its core expertise includes geometric control methods, sliding mode control, combinatorial optimisation, linear and mixed-integer optimisation, graph theory, and constrained multi-modal engineering optimisation. The group’s research is applied across a wide range of industrial and scientific domains, including robotics, aerospace, sensing systems, transportation, logistics, scheduling, materials science, computer science, and biology.
The Scientific Computing and Machine Learning Group brings together researchers working on the development of advanced computational and machine learning methods for complex problems arising in science and engineering. The group has strong expertise in numerical analysis and scientific computing, while also expanding into emerging areas of Mathematics and AI. Our research includes numerical linear algebra, numerical solutions of partial differential equations, computational biology, inverse problems, image analysis, mathematical theory for AI algorithms, and computer algebra. By combining mathematical rigour with modern data-driven techniques, we aim to develop efficient, accurate, and innovative methods for modelling, simulation, and analysis in a wide range of applications.
Research Group
The Continuum Modelling and Analysis Group studies mathematical models for complex dynamical systems arising in science and engineering. Our main focus is on nonlinear optimisation and variational analysis, particularly in settings involving nonsmooth and nonconvex phenomena. Current research directions include calculus of variations, optimal control, and deterministic as well as stochastic differential equations, together with both qualitative and quantitative aspects of dynamical systems. In addition, the group pursues research in fluid mechanics, applied harmonic and time-frequency analysis, as well as applications in signal processing, stochastic analysis, and numerical methods for differential equations.
The Control, Optimisation, and Operational Research Group specialises in linear and nonlinear dynamics, network optimisation, applied combinatorics, and dynamic optimisation. Its core expertise includes geometric control methods, sliding mode control, combinatorial optimisation, linear and mixed-integer optimisation, graph theory, and constrained multi-modal engineering optimisation. The group’s research is applied across a wide range of industrial and scientific domains, including robotics, aerospace, sensing systems, transportation, logistics, scheduling, materials science, computer science, and biology.
The Scientific Computing and Machine Learning Group brings together researchers working on the development of advanced computational and machine learning methods for complex problems arising in science and engineering. The group has strong expertise in numerical analysis and scientific computing, while also expanding into emerging areas of Mathematics and AI. Our research includes numerical linear algebra, numerical solutions of partial differential equations, computational biology, inverse problems, image analysis, mathematical theory for AI algorithms, and computer algebra. By combining mathematical rigour with modern data-driven techniques, we aim to develop efficient, accurate, and innovative methods for modelling, simulation, and analysis in a wide range of applications.