Flow decomposition for heat equations with memory

Autor:	Gengsheng Wang, Yubiao Zhang, Enrique Zuazua
Rok vydání:	2022
Předmět:	Kernel (image processing) Flow (mathematics) Component (thermodynamics) Applied Mathematics General Mathematics Mathematical analysis Null (mathematics) Decomposition (computer science) Velocity factor Heat equation Smoothing Mathematics
Zdroj:	Journal de Mathématiques Pures et Appliquées. 158:183-215
ISSN:	0021-7824
DOI:	10.1016/j.matpur.2021.11.005
Popis:	We build up a decomposition for the flow generated by the heat equation with a real analytic memory kernel. It consists of three components: The first one is of parabolic nature; the second one gathers the hyperbolic component of the dynamics, with null velocity of propagation; the last one exhibits a finite smoothing effect. This decomposition reveals the hybrid parabolic-hyperbolic nature of the flow and clearly illustrates the significant impact of the memory term on the parabolic behavior of the system in the absence of memory terms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ad0b4247b3780da5540c227117a7814f https://doi.org/10.1016/j.matpur.2021.11.005 Zobrazit plný text záznamu Full Text from ScienceDirect