# highly oscillatoryRecently Published Documents

397
(FIVE YEARS 138)

## H-INDEX

29
(FIVE YEARS 6)

2022 ◽
Vol 40 (1) ◽
pp. 70-88
Author(s):
Bin Wang & Xinyuan Wu
Keyword(s):

2022 ◽
Vol 14 (1) ◽
pp. 115
Author(s):
Sakhi Zaman ◽
Latif Ullah Khan ◽
Lucian Mihet-Popa
Keyword(s):

The paper demonstrates symmetric integral operator and interpolation based numerical approximations for linear and nonlinear ordinary differential equations (ODEs) with oscillatory factor x′=ψ(x)+χω(t), where the function χω(t) is an oscillatory forcing term. These equations appear in a variety of computational problems occurring in Fourier analysis, computational harmonic analysis, fluid dynamics, electromagnetics, and quantum mechanics. Classical methods such as Runge–Kutta methods etc. fail to approximate the oscillatory ODEs due the existence of oscillatory term χω(t). Two types of methods are presented to approximate highly oscillatory ODEs. The first method uses radial basis function interpolation, and then quadrature rules are used to evaluate the integral part of the solution equation. The second approach is more generic and can approximate highly oscillatory and nonoscillatory initial value problems. Accordingly, the first-order initial value problem with oscillatory forcing term is transformed into highly oscillatory integral equation. The transformed symmetric oscillatory integral equation is then evaluated numerically by the Levin collocation method. Finally, the nonlinear form of the initial value problems with an oscillatory forcing term is converted into a linear form using Bernoulli’s transformation. The resulting linear oscillatory problem is then computed by the Levin method. Results of the proposed methods are more reliable and accurate than some state-of-the-art methods such as asymptotic method, etc. The improved results are shown in the numerical section.

Author(s):
Xianfa Hu ◽
Xiong You ◽
Xiong You
Keyword(s):

Author(s):
Haotao Cai
Keyword(s):

2021 ◽
Vol 40 (8) ◽
Author(s):
Zhongli Liu ◽
Xiaoxue Sa ◽
Hongjiong Tian
Keyword(s):

2021 ◽
Vol 131 ◽
pp. 51-63
Author(s):
Suliman Khan ◽
Sakhi Zaman ◽
Hongchao Kang ◽
Hasrat Hussain Shah ◽
...
Keyword(s):

2021 ◽
pp. 113905
Author(s):
Jannis Körner ◽
Anton Arnold ◽
Kirian Döpfner
Keyword(s):

Author(s):
Frank Merle ◽
Pierre Raphaël ◽
Igor Rodnianski ◽
Jeremie Szeftel
Keyword(s):

AbstractWe consider the energy supercritical defocusing nonlinear Schrödinger equation \begin{aligned} i\partial _tu+\Delta u-u|u|^{p-1}=0 \end{aligned} i ∂ t u + Δ u - u | u | p - 1 = 0 in dimension $$d\ge 5$$ d ≥ 5 . In a suitable range of energy supercritical parameters (d, p), we prove the existence of $${\mathcal {C}}^\infty$$ C ∞ well localized spherically symmetric initial data such that the corresponding unique strong solution blows up in finite time. Unlike other known blow up mechanisms, the singularity formation does not occur by concentration of a soliton or through a self similar solution, which are unknown in the defocusing case, but via a front mechanism. Blow up is achieved by compression for the associated hydrodynamical flow which in turn produces a highly oscillatory singularity. The front blow up profile is chosen among the countable family of $${\mathcal {C}}^\infty$$ C ∞ spherically symmetric self similar solutions to the compressible Euler equation whose existence and properties in a suitable range of parameters are established in the companion paper (Merle et al. in Preprint (2019)) under a non degeneracy condition which is checked numerically.

2021 ◽
Author(s):
Masoud Ataei ◽
Xiaogang Wang
Keyword(s):

Abstract We propose a novel transform called Lehmer transform and establish theoretical results which are used to compress and characterize large volumes of highly volatile time series data. It will be shown that our proposed method could be used as a practical data-driven approach for analyzing extreme events in nonstationary and highly oscillatory stochastic processes such as biological signals. We demonstrate the advantage of the proposed transform in comparison with traditional methods such as Fourier and Wavelets transforms through an example of devising a classifier to discern the patients with major depressive disorder from the healthy subjects using their recorded EEG signals and provide the computational results. We show that the proposed transform can be used for building better and more robust classifiers with significant accuracy.

2021 ◽
pp. 113820
Author(s):
Jing Gao
Keyword(s):