2021
DOI: 10.1515/nleng-2021-0035
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Hybrid of differential quadrature and sub-gradients methods for solving the system of Eikonal equations

Abstract: Many important natural phenomena of wave propagations are modeled by Eikonal equations and a variety of new methods are needed to solve them. The differential quadrature method (DQM) is an effective numerical method for solving the system of differential equations that can achieve accurate numerical results using fewer grid points and therefore requires relatively little computational effort. In this paper, we focus on the implementation of the non-smooth Eikonal optimization by using a hybrid of polynomial di… Show more

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Cited by 2 publications
(2 citation statements)
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“…High speed railway signal fault diagnosis forms a turnout fault diagnosis model with deliverable evaluation indexes through the training and optimization of the fault diagnosis model based on deep learning integration [16]. The turnout fault phenomenon of high-speed railway is input into the fault diagnosis model, and the model automatically outputs the type and cause of the fault, so as to realize the intelligent diagnosis of turnout equipment fault [17][18][19]. The architecture of this research work is depicted in Figure 2.…”
Section: Methodsmentioning
confidence: 99%
“…High speed railway signal fault diagnosis forms a turnout fault diagnosis model with deliverable evaluation indexes through the training and optimization of the fault diagnosis model based on deep learning integration [16]. The turnout fault phenomenon of high-speed railway is input into the fault diagnosis model, and the model automatically outputs the type and cause of the fault, so as to realize the intelligent diagnosis of turnout equipment fault [17][18][19]. The architecture of this research work is depicted in Figure 2.…”
Section: Methodsmentioning
confidence: 99%
“…The final output of this project is a data model that can solve the sudden massive data loading and reading and writing. This model can provide good support for the background data layer of the Internet of Things monitoring system and avoid the above problems caused by data [4,5].…”
Section: Introductionmentioning
confidence: 99%