{"title":"Linearly implicit and large time-stepping conservative exponential relaxation schemes for the nonlocal cubic Gross-Pitaevskii equation","authors":"Yayun Fu, Xu Qian, Songhe Song, Dongdong Hu","doi":"10.1007/s10444-025-10238-8","DOIUrl":null,"url":null,"abstract":"<div><p>The nonlocal cubic Gross-Pitaevskii equation, in comparison to the cubic Gross-Pitaevskii equation, incorporates a nonlocal diffusion operator and can capture a wider range of practical phenomena. However, this nonlocal formulation significantly increases the computational expenses in numerical simulations, necessitating the development of efficient and accurate time integration schemes. This paper uses the relaxation method to present two linearly implicit conservative exponential schemes for the nonlocal cubic Gross-Pitaevskii equation. One proposed scheme can inherit the discrete energy while the other preserves the mass in the discrete scene. We first apply the Fourier pseudo-spectral method to the equation and derive a conservative semi-discrete system. Then, based on the ideas of the traditional relaxation method, adopting the exponential time difference method to approximate the system in time can lead to an energy-preserving exponential scheme. The mass-preserving scheme is derived by using the integral factor method to discretize the system in the temporal direction. The stability results of the constructed schemes are given. In addition, all schemes are linearly implicit and can be implemented efficiently with a large time step. Finally, numerical results show that both proposed methods are remarkably efficient and have better stability than the original relaxation scheme.</p></div>","PeriodicalId":50869,"journal":{"name":"Advances in Computational Mathematics","volume":"51 3","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Computational Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10444-025-10238-8","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
The nonlocal cubic Gross-Pitaevskii equation, in comparison to the cubic Gross-Pitaevskii equation, incorporates a nonlocal diffusion operator and can capture a wider range of practical phenomena. However, this nonlocal formulation significantly increases the computational expenses in numerical simulations, necessitating the development of efficient and accurate time integration schemes. This paper uses the relaxation method to present two linearly implicit conservative exponential schemes for the nonlocal cubic Gross-Pitaevskii equation. One proposed scheme can inherit the discrete energy while the other preserves the mass in the discrete scene. We first apply the Fourier pseudo-spectral method to the equation and derive a conservative semi-discrete system. Then, based on the ideas of the traditional relaxation method, adopting the exponential time difference method to approximate the system in time can lead to an energy-preserving exponential scheme. The mass-preserving scheme is derived by using the integral factor method to discretize the system in the temporal direction. The stability results of the constructed schemes are given. In addition, all schemes are linearly implicit and can be implemented efficiently with a large time step. Finally, numerical results show that both proposed methods are remarkably efficient and have better stability than the original relaxation scheme.
期刊介绍:
Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis.
This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.