Results 61 to 70 of about 234,207 (337)
Decomposition of Fundamental Function of Finsler Space & Some Tensors by Lie-Derivative in GBK- 5RFn
This paper discuses the decomposition of Fundamental Function of Finsler space by Lie-derivative in generalized fifth recurrent Finsler space for Cartan's fourth curvature tensor K_jkh^i in sense of Berwald.
Adel Alqashbri
doaj +1 more source
A Hybrid Norm for Guaranteed Tensor Recovery
Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
doaj +1 more source
Convex Tensor Decomposition via Structured Schatten Norm Regularization [PDF]
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tensor decomposition with wider literature on structured ...
Suzuki, Taiji, Tomioka, Ryota
core
Covariant derivative of the curvature tensor of pseudo-K\"ahlerian manifolds
It is well known that the curvature tensor of a pseudo-Riemannian manifold can be decomposed with respect to the pseudo-orthogonal group into the sum of the Weyl conformal curvature tensor, the traceless part of the Ricci tensor and of the scalar ...
A Derdzinski +19 more
core +1 more source
Phase‐field simulations coupled with dislocation‐density‐based crystal plasticity modeling reproduce γ′ rafting behavior in single‐crystal Ni‐based superalloys under varied loading conditions. The model captures both macroscopic creep and microscopic morphology evolution, with results matching high‐temperature creep experiments.
Micheal Younan +5 more
wiley +1 more source
Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation
In this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor.
Xixiang Chen +4 more
doaj +1 more source
The Tensor‐based Feature Analysis of Spatiotemporal Field Data With Heterogeneity
Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix.
Dongshuang Li +5 more
doaj +1 more source
Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition
In this article, an effective approach is proposed to recognise the 2D+3D facial expression automatically based on orthogonal Tucker decomposition using factor priors (OTDFPFER).
Yunfang Fu +4 more
doaj +1 more source
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity.
Cao, Jianting +4 more
core +1 more source
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source

