Results 11 to 20 of about 281 (108)
Deep Efficient Continuous Manifold Learning for Time Series Modeling
, 2023 Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive definite matrix is being actively studied in computer vision, signal ...
Suk, Heung-Il +3 more
core +2 more sources
The Perlick system type I: From the algebra of symmetries to the geometry of the trajectories [PDF]
, 2017 In this paper, we investigate the main algebraic properties of the maximally superintegrable system known as âPerlick system type Iâ. All possible values of the relevant parameters, K and Î2, are considered. In particular, depending on the sign of the
Kuru, Å.. +8 more
core +1 more source
Campos vectoriales tipo Killing en geometría Ruemanniana [PDF]
, 2015 En geometría Riemanniana, los vectores de Killing y las isometrías fueron inicialmente estudiados desde un punto lo cal, es decir, se estudiaban sus propiedades en vecindades y entornos de algún punto del espacio.
Clavijo Hernández, Paola Andrea
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Homogenization of Rapidly Oscillating Riemannian Manifolds
, 2021 In this thesis we study the asymptotic behavior of bi-Lipschitz diffeomorphic weighted Riemannian manifolds with techniques from the theory of homogenization.
Hoppe, Helmer
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Elastic Metrics on Spaces of Euclidean Curves: Theory and Algorithms
, 2022 A main goal in the field of statistical shape analysis is to define computable and informative metrics on spaces of immersed manifolds, such as the space of curves in a Euclidean space.
Klassen, Eric +5 more
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Optimization of 3D‐Printed Structured Packings—Current State and Future Developments
Chemie Ingenieur Technik, EarlyView.This paper gives an overview about structured packing development for distillation, surveying heuristic development cycles, computational fluid dynamics simulations, and additive manufacturing techniques. The emerging application of shape optimization to improve packings is emphasized, and its benefits, impact, and limitations are discussed.
Dennis Stucke +3 more
wiley +1 more source
Stochastic Gradient Descent in High Dimensions for Multi‐Spiked Tensor PCA
Communications on Pure and Applied Mathematics, EarlyView.ABSTRACT We study the high‐dimensional dynamics of online stochastic gradient descent (SGD) for the multi‐spiked tensor model. This multi‐index model arises from the tensor principal component analysis (PCA) problem with multiple spikes, where the goal is to estimate the unknown signal vectors within the N$N$‐dimensional unit sphere through maximum ...
Gérard Ben Arous +2 more
wiley +1 more source
Adaptive Log-Euclidean Metrics for SPD Matrix Learning
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode underlying structural correlation in data.
Sebe, Nicu +5 more
core +3 more sources
On Geometric Phase Model in the Theory of Curves With Myller Configuration
Mathematical Methods in the Applied Sciences, EarlyView.ABSTRACT In this paper, we introduce a linearly polarized light wave in an optical fiber and rotation of the polarization plane through the Frenet‐type frame with Myller configuration. Since the geometric evaluation and interpretations of a polarized light wave are associated with geometric phase, a new type of geometric phase model has been ...
Zehra İşbilir +2 more
wiley +1 more source
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source