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Randomised one-step time integration methods for deterministic operator differential equations

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Lie, Han Cheng, Stahn, Martin and Sullivan, T. J. (2022) Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, 59 (1). 13. doi:10.1007/s10092-022-00457-6 ISSN 0008-0624.

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Official URL: http://dx.doi.org/10.1007/s10092-022-00457-6

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Abstract

Uncertainty quantification plays an important role in problems that involve inferring a parameter of an initial value problem from observations of the solution. Conrad et al. (Stat Comput 27(4):1065–1082, 2017) proposed randomisation of deterministic time integration methods as a strategy for quantifying uncertainty due to the unknown time discretisation error. We consider this strategy for systems that are described by deterministic, possibly time-dependent operator differential equations defined on a Banach space or a Gelfand triple. Our main results are strong error bounds on the random trajectories measured in Orlicz norms, proven under a weaker assumption on the local truncation error of the underlying deterministic time integration method. Our analysis establishes the theoretical validity of randomised time integration for differential equations in infinite-dimensional settings.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Differential equations , Numerical analysis , Random dynamical systems
Journal or Publication Title: Calcolo
Publisher: Springer
ISSN: 0008-0624
Official Date: 25 February 2022
Dates:
DateEvent
25 February 2022Published
24 January 2022Accepted
Volume: 59
Number: 1
Article Number: 13
DOI: 10.1007/s10092-022-00457-6
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 28 February 2022
Date of first compliant Open Access: 28 February 2022
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
318763901—SFB1294[DFG] Deutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/501100001659

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