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NIRI GOVIND Environmental Molecular Sciences Laboratory Pacific Northwest National Laboratory "Exploring Valence and X-ray Spectroscopies with Real-Time and Linear-Response Time-Dependent Density Functional Theory" Wednesday, January 13, 2016 -- 3:30 P.M. 499 Dirac Science Library
The Department of Scientific Computing at Florida State University presents SCOTT MCKINLEY Department of Mathematics University of Florida "Sensing and Decision-making in Random Search" Thursday, April 18, 2013 2:00 P.M. 499 Dirac Science Library
Many organisms locate resources in environments in which sensory signals are rare, noisy, and lack directional information. Recent studies of search in such environments model search behavior using random walks (e.g., Levy walks) that match empirical movement distributions. We extend this modeling approach to include searcher responses to noisy sensory data. The results of numerical simulation show that including even a simple response to noisy sensory data can dominate other features of random search, resulting in lower mean search times and decreased risk of long intervals between target encounters. In particular, we show that a lack of signal is not a lack of information. Searchers that receive no signal can quickly abandon target-poor regions. On the other hand, receiving a strong signal leads a searcher to concentrate search effort near targets. These responses cause simulated searchers to exhibit an emergent area-restricted search behavior similar to that observed of many organisms in nature.
The Department of Scientific Computing at Florida State University presents HYUNG-CHUN LEE Director, Center for Applied Analysis and Scientific Computations Professor, Department of Mathematics Ajou University, Korea "A sparse collocation method for an optimal control problem of SPDE" Thursday, January 31, 2013 3:30 P.M. 499 Dirac Science Library
In this talk, we propose and analyze a stochastic collocation method for solving optimal control problems for elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables. We prove existence of optimal solution and derive an optimality system. In the method, we use a Galerkin approximation in space and a sparse grid collocation in the probability space. We provide error estimates for fully discrete solution using an appropriate norm and analyze the computational efficiency. Computational evidence complements the present theory and shows the effectiveness of the sparse grid stochastic collocation method.
"Multi-Compartmental Modeling of Astrocytes"
Gordon Erlebacher
Department of Scientific Computing
Florida State University
