Replication Data for: Constraint-aware neural networks for Riemann problems

DOI

Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in the README.md.

Identifier
DOI https://doi.org/10.18419/darus-3869
Related Identifier IsCitedBy https://doi.org/10.1016/j.jcp.2020.109345
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3869
Provenance
Creator Magiera, Jim M. ORCID logo
Publisher DaRUS
Contributor Magiera, Jim M.; Rohde, Christian
Publication Year 2024
Funding Reference DFG 84292822
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Magiera, Jim M. (Universität Stuttgart); Rohde, Christian (Universität Stuttgart)
Representation
Resource Type Dataset
Format application/x-tar; text/markdown
Size 778240; 21678080; 6881280; 1852
Version 1.0
Discipline Natural Sciences; Physics