{"title":"Temporal fractal nature of linearized Kuramoto–Sivashinsky SPDEs and their gradient in one-to-three dimensions","authors":"Wensheng Wang, Lu Yuan","doi":"10.1007/s40072-024-00327-y","DOIUrl":null,"url":null,"abstract":"<p>Let <span>\\(U=\\{U(t,x), (t,x)\\in \\mathring{{\\mathbb {R}}}_+\\times {\\mathbb {R}}^d\\}\\)</span> and <span>\\(\\partial _{x}U=\\{\\partial _{x}U(t,x), (t,x)\\in \\mathring{{\\mathbb {R}}}_+\\times {\\mathbb {R}}\\}\\)</span> be the solution and gradient solution of the fourth order linearized Kuramoto–Sivashinsky (L-KS) SPDE, driven by the space-time white noise in one-to-three dimensional spaces, respectively. We use the underlying explicit kernels to prove the exact global temporal moduli and temporal LILs for the L-KS SPDEs and gradient, and utilize them to prove that the sets of temporal fast points where exceptional oscillation of <span>\\(U(\\cdot ,x)\\)</span> and <span>\\(\\partial _{x}U(\\cdot ,x)\\)</span> occur infinitely often are random fractals, and evaluate their Hausdorff dimensions and their hitting probabilities. It has been confirmed that these points of <span>\\(U(\\cdot ,x)\\)</span> and <span>\\(\\partial _{x}U(\\cdot ,x)\\)</span>, in time, are everywhere dense with power of the continuum almost surely, and their hitting probabilities are determined by the packing dimension <span>\\(\\dim _{_{p}}(B)\\)</span> of the target set <i>B</i>.</p>","PeriodicalId":48569,"journal":{"name":"Stochastics and Partial Differential Equations-Analysis and Computations","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastics and Partial Differential Equations-Analysis and Computations","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s40072-024-00327-y","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
Let \(U=\{U(t,x), (t,x)\in \mathring{{\mathbb {R}}}_+\times {\mathbb {R}}^d\}\) and \(\partial _{x}U=\{\partial _{x}U(t,x), (t,x)\in \mathring{{\mathbb {R}}}_+\times {\mathbb {R}}\}\) be the solution and gradient solution of the fourth order linearized Kuramoto–Sivashinsky (L-KS) SPDE, driven by the space-time white noise in one-to-three dimensional spaces, respectively. We use the underlying explicit kernels to prove the exact global temporal moduli and temporal LILs for the L-KS SPDEs and gradient, and utilize them to prove that the sets of temporal fast points where exceptional oscillation of \(U(\cdot ,x)\) and \(\partial _{x}U(\cdot ,x)\) occur infinitely often are random fractals, and evaluate their Hausdorff dimensions and their hitting probabilities. It has been confirmed that these points of \(U(\cdot ,x)\) and \(\partial _{x}U(\cdot ,x)\), in time, are everywhere dense with power of the continuum almost surely, and their hitting probabilities are determined by the packing dimension \(\dim _{_{p}}(B)\) of the target set B.
期刊介绍:
Stochastics and Partial Differential Equations: Analysis and Computations publishes the highest quality articles presenting significantly new and important developments in the SPDE theory and applications. SPDE is an active interdisciplinary area at the crossroads of stochastic anaylsis, partial differential equations and scientific computing. Statistical physics, fluid dynamics, financial modeling, nonlinear filtering, super-processes, continuum physics and, recently, uncertainty quantification are important contributors to and major users of the theory and practice of SPDEs. The journal is promoting synergetic activities between the SPDE theory, applications, and related large scale computations. The journal also welcomes high quality articles in fields strongly connected to SPDE such as stochastic differential equations in infinite-dimensional state spaces or probabilistic approaches to solving deterministic PDEs.