Results 81 to 90 of about 32,100 (199)
Inference on Common Trends in a Cointegrated Nonlinear SVAR
ABSTRACT We consider the problem of performing inference on the number of common stochastic trends when data is generated by a cointegrated CKSVAR (a two‐regime, piecewise affine SVAR; Mavroeidis, 2021), using a modified version of the Breitung (2002) multivariate variance ratio test that is robust to the presence of nonlinear cointegration (of a known
James A. Duffy, Xiyu Jiao
wiley +1 more source
Self‐Similar Blowup for the Cubic Schrödinger Equation
ABSTRACT We give a rigorous proof for the existence of a finite‐energy, self‐similar solution to the focusing cubic Schrödinger equation in three spatial dimensions. The proof is computer‐assisted and relies on a fixed point argument that shows the existence of a solution in the vicinity of a numerically constructed approximation.
Roland Donninger, Birgit Schörkhuber
wiley +1 more source
Is It Easier to Count Communities Than Find Them?
ABSTRACT Random graph models with community structure have been studied extensively in the literature. For both the problems of detecting and recovering community structure, an interesting landscape of statistical and computational phase transitions has emerged. A natural unanswered question is: Might it be possible to infer properties of the community
Cynthia Rush +3 more
wiley +1 more source
This paper presents two operational matrices. The first one represents integer-order derivatives of the modified shifted Chebyshev polynomials of the second kind.
M. Abdelhakem +3 more
doaj +1 more source
In this paper, we present a numerical method proficient for solving a system of time–fractional partial differential equations. For this sake, we use spectral collection method based on shifted Chebyshev polynomials in space and finite difference method ...
Basim Albuohimad +2 more
doaj +1 more source
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials
ABSTRACT Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the
Farinaz Mostajeran, Salah A. Faroughi
wiley +1 more source
Collocation Method via Jacobi Polynomials for Solving Nonlinear Ordinary Differential Equations
We extend a collocation method for solving a nonlinear ordinary differential equation (ODE) via Jacobi polynomials. To date, researchers usually use Chebyshev or Legendre collocation method for solving problems in
Ahmad Imani, Azim Aminataei, Ali Imani
doaj +1 more source
Fast algorithm and new potential formula represented by Chebyshev polynomials for an [Formula: see text] globe network. [PDF]
Zhou Y, Zheng Y, Jiang X, Jiang Z.
europepmc +1 more source
Optimal Control‐Based Generic Framework for Radiofrequency Pulse Design in MRI
This paper presents an open‐source Python‐based optimal control RF design framework, which can tackle various problems (short‐T2 selective excitation or B1‐robust excitation/inversion). It features three main methodological contributions: a specific cost is introduced to reduce pulse peak amplitude; consistent integration of various hard constraints on
Emilio Molina +2 more
wiley +1 more source

