Results 91 to 100 of about 4,085 (219)
Fast Calculation for the Flow and Heat Transfer of Tempered Fractional Maxwell Viscoelastic Fluid
This study develops a tempered fractional Maxwell model to simulate unsteady thermal flow in viscoelastic fluids, capturing key rheological behaviors. A fast SOE‐based algorithm is proposed to improve the computational efficiency of the numerical scheme. Results reveal how key parameters influence fluid motion and heat transfer, demonstrating the model'
Yi Liu, Mochen Jiang, Libo Feng
wiley +1 more source
Midpoint Derivative-Based Closed Newton-Cotes Quadrature
A novel family of numerical integration of closed Newton-Cotes quadrature rules is presented which uses the derivative value at the midpoint. It is proved that these kinds of quadrature rules obtain an increase of two orders of precision over the ...
Weijing Zhao, Hongxing Li
doaj +1 more source
Quadrature Formula For Sampled Functions
This paper deals with efficient quadrature formulas involving functions that are observed only at fixed sampling points. The approach that we develop is derived from efficient continuous quadrature formulas, such as Gauss-Legendre or Clenshaw-Curtis quadrature.
Minaoui, Khalid +3 more
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Advances in Position‐Momentum Entanglement: A Versatile Tool for Quantum Technologies
Position–momentum entanglement constitutes a high‐dimensional continuous‐variable resource in quantum optics. Recent advances in its generation, characterization, and control are reviewed, with emphasis on spontaneous parametric down‐conversion and modern measurement techniques.
Satyajeet Patil +6 more
wiley +1 more source
Convergence of Gaussian Quadrature Formulas
Classical Gaussian formulas are well known. They construct a polynomial interpolating in the zeros of a polynomial orthogonal with respect to a positive measure \(\alpha\) and the integral of this polynomial is a quadrature formula for \(\int f(x) d\alpha(x)\) with maximal polynomial degree of exactness.
openaire +2 more sources
Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems
Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data.
Xupeng Cheng, Lijin Wang, Yanzhao Cao
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Quadrature formulas and taylor series of secant and tangent [PDF]
Second-order quadrature formulas and their fourth-order expansions are derived from the Taylor series of the secant and tangent functions. The errors of the approximations are compared to the error of the midpoint approximation.
Yuri Dimitrov +2 more
doaj
ABSTRACT We apply the boundary‐only isothermal formulation of finite‐distance weak gravitational deflection to regular black holes, black‐bounces, traversable wormholes, non‐asymptotically flat backgrounds, and static quadrupolar spacetimes. The methodological advance is not a new value for a known bending angle.
Reggie C. Pantig, Ali Övgün
wiley +1 more source
ABSTRACT Frequent visits in cohort studies are important as they enable, among others, the assessment of disease progression and treatment effectiveness. In HIV studies, extended intervals between visits have been shown to be associated with adverse outcomes (e.g., antiretroviral therapy interruption).
Achilleas Stamoulopoulos +2 more
wiley +1 more source
Computing the matrix exponential with the double exponential formula
This article considers the computation of the matrix exponential eA{{\rm{e}}}^{A} with numerical quadrature. Although several quadrature-based algorithms have been proposed, they focus on (near) Hermitian matrices.
Tatsuoka Fuminori +3 more
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