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In this paper, we consider the cylindrically symmetric Finsler metrics and we obtain their Douglas curvature. Furthermore, we obtain the differential equation system of the cylindrically symmetric Finsler metrics with vanishing Douglas curvature. Man
Externí odkaz:
http://arxiv.org/abs/2410.19099
We introduce the notion of the Bruce-Roberts Tjurina number for holomorphic 1-forms relative to a pair $(X,V)$ of complex analytic subvarieties. When the pair $(X,V)$ consists of isolated complex analytic hypersurfaces, we prove that the Bruce-Robert
Externí odkaz:
http://arxiv.org/abs/2409.19814
We investigate the travel time in a navigation problem from a geometric perspective. The setting involves an open subset of the Euclidean plane, representing a lake perturbed by a symmetric wind flow proportional to the distance from the origin. The
Externí odkaz:
http://arxiv.org/abs/2409.12058
We introduce the notion of the \textit{Bruce-Roberts number} for holomorphic 1-forms relative to complex analytic varieties. Our main result shows that the Bruce-Roberts number of a 1-form $\omega$ with respect to a complex analytic hypersurface $X$
Externí odkaz:
http://arxiv.org/abs/2409.01237
Autor:
León, Víctor, Scárdua, Bruno
In this paper we study holomorphic actions of the complex multiplicative group on complex manifolds around a singular (fixed) point. We prove linearization results for the germ of action and also for the whole action under some conditions on the mani
Externí odkaz:
http://arxiv.org/abs/2408.09625
Autor:
Racheline Barda
Publikováno v:
Encyclopedia of Jews in the Islamic World
Externí odkaz:
http://dx.doi.org/10.1163/1878-9781_ejiw_SIM_000881
We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries. In this setting, data is limited, and so it is desirable for models to perform well in the low-data setting. We show that Neural Basis Fun
Externí odkaz:
http://arxiv.org/abs/2311.16860
High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situat
Externí odkaz:
http://arxiv.org/abs/2311.00060