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Matthew W. Wilson (University of Kentucky)

"New Lines"

Abstract: In the twenty years that have passed since the fabled Friday Harbor meetings of November 1993, where GIS practitioners and critical human geographers agreed to a cease-fire, the GIS & Society agenda has been reflected upon, pushed forward, and diffracted in few (but intellectually significant) arenas. Critical, participatory, public participation, and feminist GIS have given way more recently to qualitative GIS, GIS and non-representational theory, and the spatial digital humanities. Traveling at the margins of these efforts has been a kind of social history of mapping and GIS. And while GIScience has been conversant and compatible with many of these permutations in the GIS & Society agenda, a social history of mapping and GIS (as signaled most directly by John Pickles in 2004) has perhaps the least potential for tinkering with GIScience practice (see recent conversation between Agnieszka Leszczynski and Jeremy Crampton in 2009). Perhaps this disconnect is growing, as can be witnessed in the feverish emergence of a ‘big data’ analytics/representation perspective within the contemporary GISciences (alongside the growth of funding paths around cyberinfrastructure). What then is the relevance and role of a social history of GIS for GIScience practice? In this presentation, I sketch and reflect upon a diversity of efforts that address this question.

Date:
Location:
Classroom Building Room 238

Analysis and PDE Seminar

Title:  Automating and Stabilizing the Discrete Empirical Interpolation Method for Nonlinear Model Reduction

Abstract:  The Discrete Empirical Interpolation Method (DEIM) is a technique for model reduction of nonlinear dynamical systems.  It is based upon a modification to proper orthogonal decomposition which is designed to reduce the computational complexity for evaluating reduced order nonlinear terms.  The DEIM approach is based upon an interpolatory projection and only requires evaluation of a few selected components of the original nonlinear term.  Thus, implementation of the reduced order nonlinear term requires a new code to be derived from the original code for evaluating the nonlinearity.  I will describe a methodology for automatically deriving a code for the reduced order nonlinearity directly from the original nonlinear code.  Although DEIM has been effective on some very difficult problems, it can under certain conditions introduce instabilities in the reduced model.  I will present a problem that has proved helpful in developing a method for stabilizing DEIM reduced models.

Date:
-
Location:
745 Patterson Office Tower
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