Results 31 to 40 of about 177,159,861 (211)

Pseudo-Lucas Functions of Fractional Degree and Applications

Axioms, 2021
In a recent article, the first and second kinds of multivariate Chebyshev polynomials of fractional degree, and the relevant integral repesentations, have been studied. In this article, we introduce the first and second kinds of pseudo-Lucas functions of
Clemente Cesarano, Pierpaolo Natalini, Paolo Emilio Ricci   +2 more
doaj   +1 more source

Eighth-Kind Chebyshev Polynomials Collocation Algorithm for the Nonlinear Time-Fractional Generalized Kawahara Equation

Fractal and Fractional, 2023
In this study, we present an innovative approach involving a spectral collocation algorithm to effectively obtain numerical solutions of the nonlinear time-fractional generalized Kawahara equation (NTFGKE).
Waleed Mohamed Abd-Elhameed, Youssri Hassan Youssri, Amr Kamel Amin, Ahmed Gamal Atta   +3 more
doaj   +1 more source

Chebyshev gradient polynomials for high resolution surface and wavefront reconstruction

, 2018
A new data processing method based on orthonormal rectangular gradient polynomials is introduced in this work. This methodology is capable of effectively reconstructing surfaces or wavefronts with data obtained from deflectometry systems, especially ...
Oh, Chang Jin, Kim, Dae Wook, Burge, James H., Smith, Greg A., Aftab, Maham, Graves, Logan R.   +5 more
core   +1 more source

Generalized Chebyshev polynomials [PDF]

, 2020
-We generalize the first and second kind Chebyshev polynomials by using the concepts and the operational formalism of the Hermite polynomials of the Kampé de Fériet type.
Clemente Cesarano
core  

Some results on complex $(p,q)-$extension $\alpha-$Chebyshev differential equation for $|x| \leq 1$ [PDF]

Journal of Mahani Mathematical Research
In this paper, we define complex $(p,q)-$extension $\alpha-$Chebyshev differential equations on $|x|\leq 1$. Our consideration is focused on determining properties of generalized Chebyshev polynomials of the first, second, third and Fourth kind, sparking
S. shojaeian, H. Mazaheri, T.S. Jesmani
doaj   +1 more source

Notes on explicit and inversion formulas for the Chebyshev polynomials of the first two kinds [PDF]

Miskolc Mathematical Notes, 2019
In the paper, starting from the Rodrigues formulas for the Chebyshev polynomials of the first and second kinds, by virtue of the Fa\`a di Bruno formula, with the help of two identities for the Bell polynomials of the second kind, and making use of a new inversion theorem for combinatorial coefficients, the authors derive two nice explicit formulas and ...
Qi, Feng, Niu, Da-Wei, Lim, Dongkyu
openaire   +2 more sources

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

A New Class of Dual-Band Waveguide Filters Based on Chebyshev Polynomials of the Second Kind

IEEE Access, 2020
This paper presents for the first time a method of mathematical synthesis involving chaining of Chebyshev polynomials of the second kind for the application of a dual-band waveguide filter. This method takes advantage of second kind Chebyshev polynomials
Yuhao Leong, Sovuthy Cheab, Socheatra Soeung, Peng Wen Wong   +3 more
doaj   +1 more source

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