Department Colloquium
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Speaker:
Dacian Daescu
Institute for Mathematics and its Applications University of Minnesota Time and Place: 3:30 p.m., Monday, January 27, 2003, 411 Allen Title: Adjoint-based techniques for the analysis of large-scale uncertain systems Abstract: Despite the increased complexity in the model representations, it is often the case that comprehensive dynamical models show poor results when compared to observational data. In four-dimensional variational data assimilation (4D-Var) a minimization algorithm is used to find the value of the model parameters such that an optimal fit between the model prediction and observations, scattered in time, is achieved. For large-scale models, the minimization of the cost functional is a very intensive computational process. The adjoint modeling is presented as a feasible tool to evaluate the sensitivity of a scalar response function with respect to a large number of model parameters. The use of a second order adjoint model to obtain Hessian information is shown to be of benefit for ill conditioned optimization problems. A research area of major interest is the design of an adaptive observational network. Expensive field-deployed resources (facilities and people) can be utilized more effectively and science success can be maximized by an optimal allocation of the observational resources. A new adjoint approach to the adaptive observations problem is presented and its potential benefits are illustrated in a comparative analysis with traditional methods based on singular vectors and gradient sensitivity. Numerical results are shown for nonlinear chemical reactions systems and atmospheric circulation models. Host: Bruce Ebanks, (662) 325-3414, ebanks@math.msstate.edu Refreshments: 4:30-5:30 p.m., 467 Allen |