2,944 research outputs found
Brane modeling in warped extra-dimension
Five-dimensional scenarios with infinitesimally thin branes replaced by
appropriate configurations of a scalar field were considered. A possibility of
periodic extra dimension was discussed in the presence on non-minimal
scalar-gravity coupling and a generalized Gibbons-Kallosh-Linde sum rule was
found. In order to avoid constraints imposed by periodicity, a non-compact
spacial extra dimension was introduced. A five dimensional model with warped
geometry and two thin branes mimicked by a scalar profile was constructed and
discussed. In the thin brane limit the model corresponds to a set-up with two
positive-tension branes. The presence of two branes allows to address the issue
of the hierarchy problem which could be solved by the standard warping of the
four dimensional metric provided the Higgs field is properly localized.
Stability of the background solution was discussed and verified in the presence
of the most general perturbations of the metric and the scalar field.Comment: 38+1 pages and 5 figures; v2: some references added and matches the
published version in JHE
Low-Speed Aeroelastic Modeling of Very Flexible Slender Wings with Deformable Airfoils
Published versio
Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data
Mathematical and Physical Sciences: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)In many applications, smooth processes generate data that is recorded under a variety of observation regimes, such as dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal uncertainty propagation, or in an application-specific manner by pooling information across subjects. We propose a unified Bayesian framework for simultaneous registration and estimation, which is flexible enough to accommodate inference on individual functions under general observation regimes. Our ability to do this relies on the specification of strongly informative prior models over the amplitude component of function variability. We provide two strategies for this critical choice: a data-driven approach that defines an empirical basis for the amplitude subspace based on available training data, and a shape-restricted approach when the relative location and number of local extrema is well-understood. The proposed methods build on the elastic functional data analysis framework to separately model amplitude and phase variability inherent in functional data. We emphasize the importance of uncertainty quantification and visualization of these two components as they provide complementary information about the estimated functions. We validate the proposed framework using simulation studies, and real applications to estimation of fractional anisotropy profiles based on diffusion tensor imaging measurements, growth velocity functions and bone mineral density curves.No embarg
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