Results 31 to 40 of about 527 (171)

Generalized Chebyshev Polynomials

Discussiones Mathematicae - General Algebra and Applications, 2018
Let h(x) be a non constant polynomial with rational coefficients. Our aim is to introduce the h(x)-Chebyshev polynomials of the first and second kind Tn and Un. We show that they are in a ℚ-vectorial subspace En(x) of ℚ[x] of dimension n.
Abchiche Mourad, Belbachir Hacéne
doaj   +1 more source

The Characteristics of the First Kind of Chebyshev Polynomials and its Relationship to the Ordinary Polynomials

JTAM (Jurnal Teori dan Aplikasi Matematika), 2021
In this article, we discuss the Chebyshev Polynomial and its characteristics. The second order difference equation and the process obtaining the explicit solution of the Chebyshev polynomial have been given for each real number. The symmetry and orthogonality of the Chebyshev polynomial has also been demonstrated using the explicit solutions obtained ...
Ikhsan Maulidi, Bonno Andri Wibowo, Vina Apriliani, Rofiqul Umam   +3 more
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Representing by several orthogonal polynomials for sums of finite products of Chebyshev polynomials of the first kind and Lucas polynomials [PDF]

Advances in Difference Equations, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Taekyun Kim, Dae San Kim, Lee-Chae Jang, D. V. Dolgy   +3 more
openaire   +3 more sources

A new characterization of ultraspherical, Hermite, and Chebyshev polynomials of the first kind

Journal of Mathematical Analysis and Applications, 2017
We show that the only polynomial sets with a generating function of the form F (xt -- R(t)) and satisfying a three-term recursion relation are the monomial set and the rescaled ultraspherical, Hermite, and Chebyshev polynomials of the first kind.
Mesk, Mohammed, Zahaf, Mohammed Brahim
openaire   +4 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Heat transfer from convecting-radiating fin through optimized Chebyshev polynomials with interior point algorithm

Nonlinear Engineering, 2019
In this paper, the problem of determining heat transfer from convecting-radiating fin of triangular and concave parabolic shapes is investigated.We consider one-dimensional, steady conduction in the fin and neglect radiative exchange between adjacent ...
Shivanian Elyas, Keshtkar Mahdi, Navidi Hamidreza   +2 more
doaj   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

Fingerprint recognition based on shark smell optimization and genetic algorithm

IJAIN (International Journal of Advances in Intelligent Informatics), 2020
Fingerprint recognition is a dominant form of biometric due to its distinctiveness. The study aims to extract and select the best features of fingerprint images, and evaluate the strength of the Shark Smell Optimization (SSO) and Genetic Algorithm (GA ...
Bakhan Tofiq Ahmed, Omar Younis Abdulhameed   +1 more
doaj   +1 more source

Derivations and identities for Chebyshev polynomials of the first and second kinds

, 2019
In this paper we follow the general approach, proposed earlier by the first author, which is derived from the invariant theory field and provides a way of obtaining of the polynomial identities for any arbitrary polynomial family. We introduce the notion of Chebyshev derivations of the first and second kinds, which is based on the polynomial algebra ...
Bedratyuk, Leonid, Luno, Nataliia
openaire   +2 more sources

Nonlinear permuted Granger causality

Canadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source