Mathematical Sciences and Computational Physics
Information on the research and impact of Mathematical sciences and computational physics at the University of Dundee.
The cluster develops mathematical and physical theories as well as numerical, computational and observational methods to understand how complex systems in biology, physics and astrophysics work covering scales from a few atoms to the entire galaxy. A key strength of this cluster is its fundamental research which gives rise to many interdisciplinary applications tying together research in Science & Engineering, Life Sciences, Medicine, and Dentistry. The cluster is closely aligned with the UKRI funding themes (especially EPSRC, STFC and BBSRC) and also benefits from the recent funding boost for the mathematical sciences institutes.
Research areas within the cluster include:
- Mathematical Biology and Computational Biophysics, developing a wide range of mathematical, physical and computational tools and techniques, including multiscale modelling and machine learning methods, aimed at quantifying and better understanding complex medical and biological systems, such as cancer (including melanoma and Glioblastoma), drug metabolism, and the immune system.
- Magnetohydrodynamics and Astrophysics, combining innovative computational and mathematical modelling with state-of-the-art observations to address problems such as the dynamics of magnetic fields in the solar atmosphere, space weather, the evolution of planetary systems and studies of star formation and stellar activity.
- Numerical Analysis and Scientific Computing, developing numerical methods and algorithms that are accurate, efficient and robust with respect to challenges that manifest in the simulation of complex phenomena arising in biological and physical applications.
Two impact case studies originating from this cluster were submitted to REF2021. One builds on the work of the late Roger Fletcher, who developed the filter method – a radically different approach to solving large and complex nonlinear optimisation problems typical of those faced by the industry. The other concerns joint research with the School of Dentistry that resulted in the development of algorithms and associated computational software for measuring dental clinical outcomes on digital dental models.
Furthermore, research in this cluster has contributed to epidemiological modelling for the MoD, modelling of COVID-19 on hospital wards, as well as patents submitted in collaboration with the Drug Discovery Unit in Life Sciences. Current impactful research includes modelling extreme events in ocean waves (rogue waves), improving data for space weather prediction (National Risk Register) and quantifying uncertainty in AI-based predictions.
Contribution to the strategic themes
Ensuring Environmental Sustainability
- efficient use of energy and resources through efficient numerical implementations of advanced mathematical and physical models
- improving the development and implementation of bio-fertilisers and hence tackling a major source of anthropomorphic CO2 and NO2 production.
Enhancing Human Health and Wellbeing
- detailed simulation of molecular interactions for designing the best drug target
- understanding the fundamental mechanisms of embryonic development for anticipating congenital defects, autoimmune diseases and cancer, inter alia
Research cluster lead
- Agis Athanassoulis
- Fordyce Davidson
- Tom Eaves
- Scott Gregory
- Eric Hall
- Gunnar Hornig
- Irene Kyza
- Ping Lin
- Soko Matsumura
- Karen Meyer
- Philip Murray
- Andrei Pisliakov
- Alexander Russell
- Aurora Sicilia-Aguilar
- Rastko Sknepnek
- Dumitru Trucu