Ian Hawke works on gravitational waves from neutron star mergers. His focus is on nonlinear numerical simulations. He teaches topics in Applied Mathematics often linked to computational, numerical, and modelling problems.
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- Numerical relativity
- Neutron Stars
- Gravitational waves
- Relativistic matter
- Numerical simulation and analysis
Ian Hawke's current research focuses on numerical simulations of dense matter in general relativity. The aim is to use results of numerical simulations combined with astrophysical observations, particularly using gravitational waves, to give information about extreme states of matter and gravity.
Recent specific areas of research link theoretical with practical aspects of complex matter interactions within current nonlinear numerical simulations. These include a variety of multiscale modelling techniques and approximations, alongside the application of modern computing architectures.
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Ian's teaching concentrates on topics in Applied Mathematics, often linked to modelling, computing, numerics, and differential equations. In recent years this has concentrated on Operational Research and Computing (MATH1058), Modelling with Differential Equations (MATH6149), and Numerical Methods (MATH3018/6141).
For projects, Ian usually supervises in similar areas of numerics and differential equations, often linked to applications in fluids (including shock waves) and gravity.
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Since building an international collaboration on simulating relativistic fluids based in Europe, Ian moved to Southampton as a Lecturer in 2005. He continues to be involved in the international Cactus project for large scale simulations and is an author and maintainer of the Whisky code for simulating relativistic hydrodynamics, and has worked on related projects such as the Carpet mesh refinement code and the Einstein toolkit. He now has research interests in all aspects of numerical simulations of neutron stars, including relativistic multifluids and solid (elastic) matter.
He is co-Director of the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling.
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