ACMS Abstracts: Spring 2015
Irene Kyza (U Dundee)
Adaptivity and blowup detection for semilinear evolution convection-diffusion equations based on a posteriori error control
We discuss recent results on the a posteriori error control and adaptivity for an evolution semilinear convection-diffusion model problem with possible blowup in finite time. This belongs to the broad class of partial differential equations describing e.g., tumor growth,chemotaxis and cell modelling. In particular, we derive a posteriori error estimates that are conditional (estimates which are valid under conditions of a posteriori type) for an interior penalty discontinuous Galerkin (dG) implicit-explicit (IMEX) method using a continuation argument. Compared to a previous work, the obtained conditions are more localised and allow the efficient error control near the blowup time. Utilising the conditional a posteriori estimator we are able to propose an adaptive algorithm that appears to perform satisfactorily. In particular, it leads to good approximation of the blowup time and of the exact solution close to the blowup. Numerical experiments illustrate and complement our theoretical results. This is joint work with A. Cangiani, E.H. Georgoulis, and S. Metcalfe from the University of Leicester.
Daniel Vimont (UW)
Linear Inverse Modeling of Central and East Pacific El Niño / Southern Oscillation (ENSO) Events
Research on the structure and evolution of individual El Niño / Southern Oscillation (ENSO) events has identified two categories of ENSO event characteristics that can be defined by maximum equatorial SST anomalies centered in the Central Pacific (around the dateline to 150 deg. W; CP events) or in the Eastern Pacific (east of about 150 deg. W; EP events). The distinction between these two events is not just academic: both types of event evolve differently, implying different predictability; the events tend to have different maximum amplitude; and the global teleconnection differs between each type of event.
In this presentation I will (i) describe the Linear Inverse Modeling (LIM) technique, (ii) apply LIM to determine an empirical dynamical operator that governs the evolution of tropical Pacific climate variability, (iii) define norms under which initial conditions can be derived that optimally lead to growth of CP or EP ENSO events, and (iv) identify patterns of stochastic forcing that are responsible for exciting each type of event.
Tao Zhou (Chinese Academy of Sciences)
The Christoffel function weighted least-squares for stochastic collocation approximations: applications to Uncertainty Quantification
We shall consider the multivariate stochastic collocation methods on unstructured grids. The motivation for such a study is the applications in parametric Uncertainty Quantification (UQ). We will first give a general framework of stochastic collocation methods, which include approaches such as compressed sensing, least-squares, and interpolation. Particular attention will be then given to the least-squares approach, and we will review recent progresses in this topic.