complete manifolds
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2021 ◽  
Vol 211 ◽  
pp. 112429
Author(s):  
Shouhei Honda ◽  
Luciano Mari ◽  
Michele Rimoldi ◽  
Giona Veronelli

Author(s):  
Yi Li

AbstractIn this paper we give local curvature estimates for the Laplacian flow on closed $$G_{2}$$ G 2 -structures under the condition that the Ricci curvature is bounded along the flow. The main ingredient consists of the idea of Kotschwar et al. (J Funct Anal 271(9):2604–2630, 2016) who gave local curvature estimates for the Ricci flow on complete manifolds and then provided a new elementary proof of Sesum’s result (Sesum in Am J Math 127(6):1315–1324, 2005), and the particular structure of the Laplacian flow on closed $$G_{2}$$ G 2 -structures. As an immediate consequence, this estimates give a new proof of Lotay and Wei’s (Geom Funct Anal 27(1):165–233, 2017) result which is an analogue of Sesum’s theorem. The second result is about an interesting evolution equation for the scalar curvature of the Laplacian flow of closed $$G_{2}$$ G 2 -structures. Roughly speaking, we can prove that the time derivative of the scalar curvature $$R_{g(t)}$$ R g ( t ) is equal to the Laplacian of $$R_{g(t)}$$ R g ( t ) , plus an extra term which can be written as the difference of two nonnegative quantities.


2021 ◽  
Author(s):  
Bruno Bianchini ◽  
Luciano Mari ◽  
Patrizia Pucci ◽  
Marco Rigoli

Filomat ◽  
2021 ◽  
Vol 35 (5) ◽  
pp. 1453-1463
Author(s):  
Omar Benslimane ◽  
Ahmed Aberqi ◽  
Jaouad Bennouna

The paper deals with the existence and uniqueness of a non-trivial solution to non-homogeneous p(x)- Laplacian equations, managed by non polynomial growth operator in the framework of variable exponent Sobolev spaces on Riemannian manifolds. The mountain pass Theorem is used.


2019 ◽  
Vol 57 (1) ◽  
pp. 71-94 ◽  
Author(s):  
Qun Chen ◽  
Jürgen Jost ◽  
Hongbing Qiu

Abstract VT-harmonic maps generalize the standard harmonic maps, with respect to the structure of both domain and target. These can be manifolds with natural connections other than the Levi-Civita connection of Riemannian geometry, like Hermitian, affine or Weyl manifolds. The standard harmonic map semilinear elliptic system is augmented by a term coming from a vector field V on the domain and another term arising from a 2-tensor T on the target. In fact, this geometric structure then also includes other geometrically defined maps, for instance magnetic harmonic maps. In this paper, we treat VT-harmonic maps and their parabolic analogues with PDE tools. We establish a Jäger–Kaul type maximum principle for these maps. Using this maximum principle, we prove an existence theorem for the Dirichlet problem for VT-harmonic maps. As applications, we obtain results on Weyl/affine/Hermitian harmonic maps between Weyl/affine/Hermitian manifolds, as well as on magnetic harmonic maps from two-dimensional domains. We also derive gradient estimates and obtain existence results for such maps from noncompact complete manifolds.


2019 ◽  
Vol 219 (1) ◽  
pp. 179-217 ◽  
Author(s):  
Gregory R. Chambers ◽  
Yevgeny Liokumovich

Author(s):  
Zhiwu Huang ◽  
Jiqing Wu ◽  
Luc Van Gool

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to achieve a tractable objective under the WGAN framework. In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.


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