Dr. Chris Cantwell is a Senior Lecturer in the Department of Aeronautics at Imperial College London, United Kingdom. Dr. Cantwell received an MMath in Mathematics in 2005 and an MSc in Scientific Computing in 2006, from the University of Warwick. He received a PhD in Scientific Computing in 2009, also from the University of Warwick, investigating the stability and transient growth of perturbations in fluid flow. He moved to Imperial College London developing high-order spectral/hp element methods, followed by a discipline-hop award from the British Heart Foundation to develop an interdisciplinary research programme in the field of cardiac electrophysiology and is a founding member of the ElectroCardioMaths programme, part of the Imperial College Centre for Cardiac Engineering.
Dr. Chris Cantwell’s research is centred around developing novel and scalable numerical approaches for efficiently modelling and understanding complex physical processes in the aerodynamics and biomedical domains. Much of his work to date has focused on the efficient implementation and application of spectral/hp element methods for performing high-fidelity numerical simulation and making these tools more accessible to users without a detailed understanding of the numerical methods. However, his research interests now extend to the fusion of numerical modelling with statistical methods and machine learning. He is a strong proponent of open-source software and is a project leader of the Nektar spectral/hp element framework which acts as a vehicle for much of his research.
et al., 2019, On weak Dirichlet boundary conditions for elliptic problems in the continuous Galerkin method, Journal of Computational Physics, Vol:394, ISSN:0021-9991, Pages:732-744
et al., 2019, Voltage during atrial fibrillation is superior to voltage during sinus rhythm in localizing areas of delayed enhancement on magnetic resonance imaging: An assessment of the posterior left atrium in patients with persistent atrial fibrillation, Heart Rhythm, ISSN:1547-5271
et al., 2019, Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling, Computers in Biology and Medicine, Vol:104, ISSN:0010-4825, Pages:339-351