Gutenberg Open Science: Applications of limiters, neural networks and polynomial annihilation in higher-order FD/FV schemes

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9856

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DC Field	Value	Language
dc.contributor.author	Hillebrand, Dorian	-
dc.contributor.author	Klein, Simon-Christian	-
dc.contributor.author	Öffner, Philipp	-
dc.date.accessioned	2024-01-05T09:58:11Z	-
dc.date.available	2024-01-05T09:58:11Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9874	-
dc.description.abstract	The construction of high-order structure-preserving numerical schemes to solve hyperbolic conservation laws has attracted a lot of attention in the last decades and various different ansatzes exist. In this paper, we compare several completely different approaches, i.e. deep neural networks, limiters and the application of polynomial annihilation to construct high-order accurate shock capturing finite difference/volume (FD/FV) schemes. We further analyze their analytical and numerical properties. We demonstrate that all techniques can be used and yield highly efficient FD/FV methods but also come with some additional drawbacks which we point out. Our investigation of the different strategies should lead to a better understanding of those techniques and can be transferred to other numerical methods as well which use similar ideas.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	510 Mathematik	de_DE
dc.subject.ddc	510 Mathematics	en_GB
dc.title	Applications of limiters, neural networks and polynomial annihilation in higher-order FD/FV schemes	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9856	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Journal of scientific computing	de
jgu.journal.volume	97	de
jgu.pages.alternative	13	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer Science + Business Media B.V.	de
jgu.publisher.place	New York, NY [u.a.]	de
jgu.publisher.issn	1573-7691	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	510	de
jgu.publisher.doi	10.1007/s10915-023-02322-2	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Naturwissenschaften	de
Appears in collections:	DFG-491381577-H

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