dc.contributor.author | Schmitz, Benedikt | |
dc.date.accessioned | 2023-06-23T09:03:48Z | |
dc.date.available | 2023-06-23T09:03:48Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3905 | |
dc.identifier.uri | https://doi.org/10.48328/tudatalib-1185 | |
dc.description | Models and scripts to calculate an artifical neural network model for neutron production yields. | de_DE |
dc.relation | IsDerivedFrom;URL;https://git.rwth-aachen.de/surrogat-models/surrogate-modeling-for-neutron-production | |
dc.rights | Creative Commons Attribution 4.0 | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject.classification | 3.24-01 Kern- und Elementarteilchenphysik, Quantenmechanik, Relativitätstheorie, Felder | de_DE |
dc.subject.ddc | 530 | |
dc.title | Modeling a Neutron Yield Model | de_DE |
dc.type | Software | de_DE |
dc.type | Model | de_DE |
tud.project | Land Hessen | LOEWE-Schwerpunkt Nukleare Photonik | TP A1 | de_DE |
tud.project | Land Hessen | LOEWE-Schwerpunkt Nukleare Photonik | TP C2 | de_DE |
tud.unit | TUDa | |
tud.history.classification | Version=2020-2024;309-01 Kern- und Elementarteilchenphysik, Quantenmechanik, Relativitätstheorie, Felder | |