25,757 research outputs found
Cone fields and topological sampling in manifolds with bounded curvature
Often noisy point clouds are given as an approximation of a particular
compact set of interest. A finite point cloud is a compact set. This paper
proves a reconstruction theorem which gives a sufficient condition, as a bound
on the Hausdorff distance between two compact sets, for when certain offsets of
these two sets are homotopic in terms of the absence of {\mu}-critical points
in an annular region. Since an offset of a set deformation retracts to the set
itself provided that there are no critical points of the distance function
nearby, we can use this theorem to show when the offset of a point cloud is
homotopy equivalent to the set it is sampled from. The ambient space can be any
Riemannian manifold but we focus on ambient manifolds which have nowhere
negative curvature. In the process, we prove stability theorems for
{\mu}-critical points when the ambient space is a manifold.Comment: 20 pages, 3 figure
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Reconstruction of the equilibrium of the plasma in a Tokamak and identification of the current density profile in real time
The reconstruction of the equilibrium of a plasma in a Tokamak is a free
boundary problem described by the Grad-Shafranov equation in axisymmetric
configuration. The right-hand side of this equation is a nonlinear source,
which represents the toroidal component of the plasma current density. This
paper deals with the identification of this nonlinearity source from
experimental measurements in real time. The proposed method is based on a fixed
point algorithm, a finite element resolution, a reduced basis method and a
least-square optimization formulation. This is implemented in a software called
Equinox with which several numerical experiments are conducted to explore the
identification problem. It is shown that the identification of the profile of
the averaged current density and of the safety factor as a function of the
poloidal flux is very robust
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