2024-03-29T14:10:38Z
https://oai.zbmath.org/v1/
oai:zbmath.org:7658099
2023-03-01T08:06:26Z
74
92
Böhringer, Pauline; Sommer, Daniel; Haase, Thomas; Barteczko, Martin; Sprave, Joachim; Stoll, Markus; Karadogan, Celalettin; Koch, David; Middendorf, Peter; Liewald, Mathias
2023
7658099
English
Elsevier (North-Holland), Amsterdam
https://zbmath.org/07658099
Content generated by zbMATH Open, such as reviews,
classifications, software, or author disambiguation data,
are distributed under CC-BY-SA 4.0. This defines the license for the
whole dataset, which also contains non-copyrighted bibliographic
metadata and reference data derived from I4OC (CC0). Note that the API
only provides a subset of the data in the zbMATH Open Web interface. In
several cases, third-party information, such as abstracts, cannot be
made available under a suitable license through the API. In those cases,
we replaced the data with the string 'zbMATH Open Web Interface contents
unavailable due to conflicting licenses.'
Comput. Methods Appl. Mech. Eng. 406, Article ID 115894, 20 p. (2023)
74-XX; 92-XX
A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments
j