{"title":"无界域上非自治分式随机反应-扩散方程的回拉测度吸引子","authors":"Shaoyue Mi, Ran Li, Dingshi Li","doi":"10.1007/s00245-024-10196-5","DOIUrl":null,"url":null,"abstract":"<div><p>This paper is concerned with the pullback measure attractors of the non-autonomous fractional reaction-diffusion equations defined on <span>\\(\\mathbb {R}^{n}\\)</span>. We first prove the existence and uniqueness of pullback measure attractors for such equations. Then we establish the upper semi-continuity of these attractors as the noise intensity <span>\\(\\varepsilon \\)</span> tends to zero. Specifically, we apply the uniform estimates on the tails of solutions to prove the asymptotic compactness of a family of probability distributions of solutions to overcome the non-compactness of usual Sobolev embeddings on unbounded domains.</p></div>","PeriodicalId":55566,"journal":{"name":"Applied Mathematics and Optimization","volume":"90 3","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pullback Measure Attractors for Non-autonomous Fractional Stochastic Reaction-Diffusion Equations on Unbounded Domains\",\"authors\":\"Shaoyue Mi, Ran Li, Dingshi Li\",\"doi\":\"10.1007/s00245-024-10196-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper is concerned with the pullback measure attractors of the non-autonomous fractional reaction-diffusion equations defined on <span>\\\\(\\\\mathbb {R}^{n}\\\\)</span>. We first prove the existence and uniqueness of pullback measure attractors for such equations. Then we establish the upper semi-continuity of these attractors as the noise intensity <span>\\\\(\\\\varepsilon \\\\)</span> tends to zero. Specifically, we apply the uniform estimates on the tails of solutions to prove the asymptotic compactness of a family of probability distributions of solutions to overcome the non-compactness of usual Sobolev embeddings on unbounded domains.</p></div>\",\"PeriodicalId\":55566,\"journal\":{\"name\":\"Applied Mathematics and Optimization\",\"volume\":\"90 3\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Mathematics and Optimization\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00245-024-10196-5\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mathematics and Optimization","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s00245-024-10196-5","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
Pullback Measure Attractors for Non-autonomous Fractional Stochastic Reaction-Diffusion Equations on Unbounded Domains
This paper is concerned with the pullback measure attractors of the non-autonomous fractional reaction-diffusion equations defined on \(\mathbb {R}^{n}\). We first prove the existence and uniqueness of pullback measure attractors for such equations. Then we establish the upper semi-continuity of these attractors as the noise intensity \(\varepsilon \) tends to zero. Specifically, we apply the uniform estimates on the tails of solutions to prove the asymptotic compactness of a family of probability distributions of solutions to overcome the non-compactness of usual Sobolev embeddings on unbounded domains.
期刊介绍:
The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.