Optimal Sampling of Random Fields for Topological Analysis

dc.contributor.advisorWanner, Thomas
dc.contributor.authorCochran, Gregory Scott
dc.creatorCochran, Gregory Scott
dc.date2011-07-26
dc.date.accessioned2011-08-22T15:38:46Z
dc.date.availableNO_RESTRICTION
dc.date.available2011-08-22T15:38:46Z
dc.date.issued2011-08-22
dc.description.abstractAlgebraic topology is becoming an increasing important tool in applied mathematics. In particular, homology theory allows one to distinguish different topologies while being tractable to compute. An important application is the study of nodal domains for solutions to stochastic partial differential equations. These are the sets where the function value is greater than zero and less than zero. In order to compute the homology of the nodal sets computationally, we must discretize the domains. However, in this discretization process, we can make mistakes in the topology. Can we develop a method that will allow us to determine a proper discretization size a priori? One approach is to use an algorithm that is guaranteed to return the correct homology. The original algorithm has a few shortcomings. We will present these shortcomings and develop methods to overcome these issues. The other approach is to establish explicit probability bounds for the making the correct homology. This is an a priori approach that will returns the probability for a fixed discretization size and also determines the optimal location of the sampled points.
dc.identifier.urihttps://hdl.handle.net/1920/6589
dc.language.isoen_US
dc.subjectTopology
dc.subjectHomology
dc.subjectApplied Mathematics
dc.subjectSampling
dc.titleOptimal Sampling of Random Fields for Topological Analysis
dc.typeDissertation
thesis.degree.disciplineMathematics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral
thesis.degree.namePhD in Mathematics

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