Summary
I'm a software developer and researcher at NVIDIA, and was previously an Assistant Professor at Durham University in the Department of Computer Science and a PDRA at Imperial working with David Ham and Paul Kelly. My research is in high performance computing and computational mathematics. Much of my recent focus has been in the development of compilers and software abstractions for the development of numerical models implemented using the finite element method. This research is concretely realised in the open source Firedrake project. I am particularly interested in preconditioning techniques for challenging problems in computational and atmospheric fluid dynamics.
Biography
- Senior software engineer, RAPIDS, NVIDIA 2022-
- Assistant Professor, Department of Computer Science, Durham University 2018-2022
- PDRA, Department of Computing, Imperial College 2013-2018
- Applications Consultant, EPCC, The University of Edinburgh 2009-2013
- PhD Student, Institute for Condensed Matter and Complex Systems, The University of Edinburgh 2005-2009
- MPhys, The University of Edinburgh 2001-2005
Publications
Journals
Farrell PE, Mitchell L, Scott LR, 2022, Two conjectures on the Stokes complex in three dimensions on Freudenthal meshes
Crum J, Cheng C, Ham DA, et al., 2022, Bringing Trimmed Serendipity Methods to Computational Practice in Firedrake, Acm Transactions on Mathematical Software, Vol:48, ISSN:0098-3500
Laakmann F, Farrell PE, Mitchell L, 2022, AN AUGMENTED LAGRANGIAN PRECONDITIONER FOR THE MAGNETOHYDRODYNAMICS EQUATIONS AT HIGH REYNOLDS AND COUPLING NUMBERS, Siam Journal on Scientific Computing, Vol:44, ISSN:1064-8275, Pages:B1018-B1044
Farrell PE, Knepley MG, Mitchell L, et al., 2021, PCPATCH: Software for the Topological Construction of Multigrid Relaxation Methods, Acm Transactions on Mathematical Software, Vol:47, ISSN:0098-3500
Farrell PE, Mitchell L, Scott R, et al., 2021, A Reynolds-robust preconditioner for the Scott-Vogelius discretization of the stationary incompressible Navier-Stokes equations, Smai Journal of Computational Mathematics, Vol:7, Pages:75-96