The Parallel Full Approximation Scheme in Space and Time
Staff - Faculty of Informatics
Start date: 24 November 2011
End date: 25 November 2011
The Faculty of Informatics is pleased to announce a seminar given by Matthew W. Emmett
DATE: Thursday, November 24th, 2011
PLACE: USI Università della Svizzera italiana, room A32, Red building (Via G. Buffi 13)
The Parallel Full Approximation Scheme in Space and Time (PFASST) algorithm for parallelizing PDEs in time is presented. Several applications of the algorithm will also be presented, including the Kuramoto-Silvashinsky and Navier-Stokes equations.
To efficiently parallelize PDEs in time, the PFASST algorithm decomposes the time domain into several time slices. After a provisional solution is obtained using a relatively inexpensive time integration scheme, the solution is iteratively improved using a deferred correction scheme. To further improve parallel efficiency,
the PFASST algorithm uses a heirarchy of discretizations at different spatial and temporal resolutions and employs an analog of the multi-grid full approximation scheme to transfer information between the discretizations.
Applications of the PFASST algorithm will be presented for several PDEs including the Kuramoto-Silvashinsky and Navier-Stokes equations. Convergence results, parallel speedups and parallel efficiencies will be reported.
2010 PhD at University of Alberta, Edmonton AB.
Applied Mathematics. Fluid mechanics (shallow-water sediment transport) and numerical analysis (WENO methods). Supervisor: Dr. T. B. Moodie.
Numerical Analysis: Parallel in Time algorithms for PDEs. Efficient implementation of Finite Volume and Finite Element schemes. Weighted Essentially Non-Oscillatory schemes for hyperbolic systems.
Partial Differential Equations: Systems of hyperbolic conservation and balance laws, perturbation theory, Sobolev spaces, and weak solutions.
Fluid Mechanics: Fluid dynamics, geophysical and environmental fows, gravity currents and sediment transport, free boundary fows, turbulence, and applications in biology.
Non-linear Dynamics and Chaos: Fixed point stability, bifurcations, and simple examples of the onset of chaos.
Differentiable Manifolds: Hamiltonian mechanics, Lie groups, holonomic and non-holonomic reduction of constraints.
Traffic Modeling: Incorporating stochastic phenomena into hyperbolic models of traffic fow.
HOST: Prof. Rolf Krause