Publication:
Regularization of the backward stochastic heat conduction problem

datacite.subject.fos oecd::Engineering and technology
dc.contributor.author Nguyen Huy Tuan
dc.contributor.author Daniel Lesnic
dc.contributor.author Tran Ngoc Thach
dc.contributor.author Tran Bao Ngoc
dc.date.accessioned 2022-11-09T07:57:10Z
dc.date.available 2022-11-09T07:57:10Z
dc.date.issued 2021
dc.description.abstract In this paper, we study the backward problem for the stochastic parabolic heat equation driven by a Wiener process. We show that the problem is ill-posed by violating the continuous dependence on the input data. In order to restore stability, we apply a filter regularization method which is completely new in the stochastic setting. Convergence rates are established under different a priori assumptions on the sought solution.
dc.identifier.doi 10.1515/jiip-2020-0013
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/1070
dc.language.iso en_US
dc.relation.ispartof Journal of Inverse and Ill-posed Problems
dc.relation.issn 0928-0219
dc.relation.issn 1569-3945
dc.subject Stochastic parabolic equations
dc.subject backward problems
dc.subject regularization
dc.subject error estimates
dc.title Regularization of the backward stochastic heat conduction problem
dc.type journal-article
dspace.entity.type Publication
oaire.citation.issue 3
oaire.citation.volume 30
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