{"title":"白噪声驱动的随机亥姆霍兹方程的有限差分法","authors":"Yanzhen Cui, Shibing Tang , Chao Zhang","doi":"10.1016/j.cam.2024.116286","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we propose two numerical methods for the stochastic Helmholtz equation driven by white noise. We obtain the approximate stochastic problem by approximating the white noise with piecewise constant process, provide some regularity of its solution and the truncation error between the approximate stochastic problem and the original problem. The limitation on the wave number <span><math><mi>k</mi></math></span> of the finite difference method (FDM) is analyzed and a stochastic finite difference (SFD) scheme is presented. The error analysis shows that the stochastic finite difference method is efficient with a certain convergence rate. Numerical experiments are provided to examine our theoretical results.</div></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Finite difference methods for stochastic Helmholtz equation driven by white noise\",\"authors\":\"Yanzhen Cui, Shibing Tang , Chao Zhang\",\"doi\":\"10.1016/j.cam.2024.116286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, we propose two numerical methods for the stochastic Helmholtz equation driven by white noise. We obtain the approximate stochastic problem by approximating the white noise with piecewise constant process, provide some regularity of its solution and the truncation error between the approximate stochastic problem and the original problem. The limitation on the wave number <span><math><mi>k</mi></math></span> of the finite difference method (FDM) is analyzed and a stochastic finite difference (SFD) scheme is presented. The error analysis shows that the stochastic finite difference method is efficient with a certain convergence rate. Numerical experiments are provided to examine our theoretical results.</div></div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S037704272400534X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S037704272400534X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
摘要
本文针对白噪声驱动的随机亥姆霍兹方程提出了两种数值方法。我们通过用片断常数过程逼近白噪声得到近似随机问题,给出了其解的一些规律性以及近似随机问题与原问题之间的截断误差。分析了有限差分法(FDM)对波数 k 的限制,并提出了一种随机有限差分法(SFD)方案。误差分析表明,随机有限差分法具有一定的收敛效率。我们还提供了数值实验来检验我们的理论结果。
Finite difference methods for stochastic Helmholtz equation driven by white noise
In this paper, we propose two numerical methods for the stochastic Helmholtz equation driven by white noise. We obtain the approximate stochastic problem by approximating the white noise with piecewise constant process, provide some regularity of its solution and the truncation error between the approximate stochastic problem and the original problem. The limitation on the wave number of the finite difference method (FDM) is analyzed and a stochastic finite difference (SFD) scheme is presented. The error analysis shows that the stochastic finite difference method is efficient with a certain convergence rate. Numerical experiments are provided to examine our theoretical results.