scholarly journals Estimates for a geometric flow for the Type IIB string

2021 ◽  
Author(s):  
Teng Fei ◽  
Duong H. Phong ◽  
Sebastien Picard ◽  
Xiangwen Zhang
Keyword(s):  
Geometric Flow

A hyperbolic geometric flow for evolving films and foams

2017 ◽  
Vol 36 (6) ◽  
pp. 1-11 ◽  
Author(s):  
Sadashige Ishida ◽  
Masafumi Yamamoto ◽  
Ryoichi Ando ◽  
Toshiya Hachisuka
Keyword(s):  
Geometric Flow

Geometric flow properties of the germanate analog of forsterite

1978 ◽  
Vol 46 (1-2) ◽  
pp. 187-196 ◽  
Author(s):  
P.J. Vaughan ◽  
R.S. Coe
Keyword(s):  
Flow Properties ◽  
Geometric Flow

scholarly journals BACH FLOWS OF PRODUCT MANIFOLDS

2012 ◽  
Vol 09 (05) ◽  
pp. 1250039 ◽  
Author(s):  
SANJIT DAS ◽  
SAYAN KAR
Keyword(s):  
Dynamical System ◽  
Fixed Point ◽  
Ricci Flow ◽  
Flow Characteristics ◽  
Geometric Flow ◽  
Flow Equations ◽  
First Order ◽  
Bach Tensor ◽  
Point Condition ◽  
Future Work

We investigate various aspects of a geometric flow defined using the Bach tensor. First, using a well-known split of the Bach tensor components for (2, 2) unwarped product manifolds, we solve the Bach flow equations for typical examples of product manifolds like S2 × S2, R2 × S2. In addition, we obtain the fixed-point condition for general (2, 2) manifolds and solve it for a restricted case. Next, we consider warped manifolds. For Bach flows on a special class of asymmetrically warped 4-manifolds, we reduce the flow equations to a first-order dynamical system, which is solved exactly to find the flow characteristics. We compare our results for Bach flow with those for Ricci flow and discuss the differences qualitatively. Finally, we conclude by mentioning possible directions for future work.

scholarly journals On a Minkowski geometric flow in the plane: Evolution of curves with lack of scale invariance

2018 ◽  
Vol 99 (1) ◽  
pp. 31-51
Author(s):  
Serena Dipierro ◽  
Matteo Novaga ◽  
Enrico Valdinoci
Keyword(s):  
Scale Invariance ◽  
Geometric Flow

scholarly journals Single-image geometric-flow x-ray speckle tracking

2018 ◽  
Vol 98 (5) ◽  
Author(s):  
David M. Paganin ◽  
Hélène Labriet ◽  
Emmanuel Brun ◽  
Sebastien Berujon
Keyword(s):  
Speckle Tracking ◽  
Single Image ◽  
Geometric Flow ◽  
X Ray

RESULTS RELATED TO PRESCRIBING PSEUDO-HERMITIAN SCALAR CURVATURE

2013 ◽  
Vol 24 (03) ◽  
pp. 1350020 ◽  
Author(s):  
PAK TUNG HO
Keyword(s):  
Scalar Curvature ◽  
Smooth Function ◽  
The Other ◽  
Cr Manifold ◽  
Geometric Flow ◽  
Yamabe Invariant ◽  
Uniqueness Results ◽  
Other Hand

In this paper, we consider the problem of prescribing pseudo-Hermitian scalar curvature on a compact strictly pseudoconvex CR manifold M. Using geometric flow, we prove that for any negative smooth function f we can prescribe the pseudo-Hermitian scalar curvature to be f, provided that dim M = 3 and the CR Yamabe invariant of M is negative. On the other hand, we establish some uniqueness and non-uniqueness results on prescribing pseudo-Hermitian scalar curvature.

scholarly journals A geometric flow-based approach for diffusion tensor image segmentation

2008 ◽  
Vol 366 (1874) ◽  
pp. 2279-2292 ◽  
Author(s):  
Weihong Guo ◽  
Yunmei Chen ◽  
Qingguo Zeng
Keyword(s):  
Magnetic Resonance ◽  
Similarity Measure ◽  
Diffusion Tensor ◽  
Three Dimensional ◽  
Positive Definite Matrix ◽  
Magnetic Resonance Images ◽  
Cerebral White Matter ◽  
Geometric Flow ◽  
Proposed Model ◽  
Consistency Measure

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3×3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.

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