Partial Differential Equations with Random Initial Data

Gi-Ren Liu

ISBN 10: 3639704762 / ISBN 13: 9783639704761

Published by Scholars' Press Feb 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -We study the limiting distributions of the solution of fractional diffusion-wave systems with subordinated Gaussian random initial data. In our work, we use homogeneous random field to model the initial data and apply the spectral representation method and multiple Wiener integrals to studying the covariance matrix functions of the random solution. The limiting distributions of the solution are obtained from different viewpoints including macroscopic scales and microscopic scales. When the initial data is weakly dependent, our results can be thought of as a generalized central limit theorem. There are two key points for this new result. The first one is that the initial data is modeled by two cross-correlated random fields, which is analyzed by the method of Feynman diagrams. Second, the limit of the solution under the macroscopic/microscopic coordinate systems is represented by a series of mutually independent Gaussian random fields. When the initial data is long-range dependent. We found a competition relationship between the effects coming from the components of the random initial data, i.e., the limiting distribution of the solution is determined by one of the components. 148 pp. Englisch.

Seller Inventory # 9783639704761

Bibliographic Details

• Title: Partial Differential Equations with Random Initial Data
• Publisher: Scholars' Press Feb 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3639704762
• ISBN 13: 9783639704761
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

We study the limiting distributions of the solution of fractional diffusion-wave systems with subordinated Gaussian random initial data. In our work, we use homogeneous random field to model the initial data and apply the spectral representation method and multiple Wiener integrals to studying the covariance matrix functions of the random solution. The limiting distributions of the solution are obtained from different viewpoints including macroscopic scales and microscopic scales. When the initial data is weakly dependent, our results can be thought of as a generalized central limit theorem. There are two key points for this new result. The first one is that the initial data is modeled by two cross-correlated random fields, which is analyzed by the method of Feynman diagrams. Second, the limit of the solution under the macroscopic/microscopic coordinate systems is represented by a series of mutually independent Gaussian random fields. When the initial data is long-range dependent. We found a competition relationship between the effects coming from the components of the random initial data, i.e., the limiting distribution of the solution is determined by one of the components.

About the Author

Gi-Ren Liu received his Ph.D. degree from National Taiwan University in 2013. Currently, he served as a post-doctoral fellow in Department of Computer Science, National Chiao Tung University. His current research interests include queuing theory, Markov decision processes and their applications on personal communications services.