Results 141 to 150 of about 411,141 (342)
A characterization of classical orthogonal Laurent polynomials
Indagationes Mathematicae (Proceedings), 1988 AbstractIn [3] certain Laurent polynomials of 2F1 genus were called “Jacobi Laurent polynomials”. These Laurent polynomials belong to systems which are orthogonal with respect to a moment sequence ((a)n/(c)n)nεℤ where a, c are certain real numbers. Together with their confluent forms, belonging to systems which are orthogonal with respect to 1/(c)n)nεℤ
openaire +2 more sources
Polymer Composites, EarlyView.AI/ML and RSM‐Driven Optimization of Polymeric Nanocomposites for Enhanced Properties. Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for the optimization of fabrication and the enhancement of the properties of polymeric nanocomposites.
Yasir Raza +9 more
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Ain Shams Engineering Journal, 2018 The theme of this paper is to analyze and compare the pulse compression with classical orthogonal polynomials (Chebyshev, Laguerre, Legendre and Hermite polynomials) of different orders.
Ankur Thakur, Salman Raju Talluri
doaj
Umbral "classical" polynomials [PDF]
arXiv, 2014 We study the umbral "classical" orthogonal polynomials with respect to a generalized derivative operator $\cal D$ which acts on monomials as ${\cal D} x^n = \mu_n x^{n-1}$ with some coefficients $\mu_n$. Let $P_n(x)$ be a set of orthogonal polynomials. Define the new polynomials $Q_n(x) =\mu_{n+1}^{-1}{\cal D} P_{n+1}(x)$.
arxiv
Statistical Complexity of Quantum Learning
Advanced Quantum Technologies, EarlyView.The statistical performance of quantum learning is investigated as a function of the number of training data N$N$, and of the number of copies available for each quantum state in the training and testing data sets, respectively S$S$ and V$V$. Indeed, the biggest difference in quantum learning comes from the destructive nature of quantum measurements ...
Leonardo Banchi +3 more
wiley +1 more source
Krall--type Orthogonal Polynomials in several variables [PDF]
arXiv, 2007 For a bilinear form obtained by adding a Dirac mass to a positive definite moment functional in several variables, explicit formulas of orthogonal polynomials are derived from the orthogonal polynomials associated with the moment functional. Explicit formula for the reproducing kernel is also derived and used to establish certain inequalities for ...
arxiv
Generating functions of Appell form for the classical orthogonal polynomials [PDF]
, 1956 R. C. T. Smith
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What can we Learn from Quantum Convolutional Neural Networks?
Advanced Quantum Technologies, EarlyView.Quantum Convolutional Neural Networks have been long touted as one of the premium architectures for quantum machine learning (QML). But what exactly makes them so successful for tasks involving quantum data? This study unlocks some of these mysteries; particularly highlighting how quantum data embedding provides a basis for superior performance in ...
Chukwudubem Umeano +3 more
wiley +1 more source
The relativistic Laguerre polynomials [PDF]
Rendiconti di Matematica e delle Sue Applicazioni, 1996 A new relativistic-type polynomial system is defined by means of the Relativistic Hermite Polynomial system, introduced recently by V.Aldaya et al. to express the wave functions of the quantum relativistic harmonic oscillator.
P. NATALINI
doaj
A generalization of classical orthogonal polynomials and the convergence of simultaneous Pade approximants [PDF]
, 1989 В. Н. Сорокин
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