191 to 200 of 1,005 Results
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
XZ Archive - 29.7 MB -
MD5: ebec1f3df0ed938dbb9b116a5e5f578c
Code taken from this commit:
https://github.com/davidscn/aste/commit/5833002c1d1af9ba3cc79401f783b6335b4e1487 |
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
XZ Archive - 44.7 KB -
MD5: dc0ef6e897e0675cf25954eb0ad4de2a
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Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
Gzip Archive - 2.9 MB -
MD5: 5797e0f5d1b71715c2e1a6610efe4cac
Code taken from https://github.com/ginkgo-project/ginkgo using commit "a195f856e" |
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
XZ Archive - 75.5 KB -
MD5: 79744b4caab09aa79ca778a5e150766c
Code taken from this commit:
https://github.com/davidscn/matrix-free-dealii-precice/commit/c4c53bbcab9e31ca2e0875c624e5b59a1ef8c695 |
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
7Z Archive - 277.8 MB -
MD5: f0de937ed1d7067e46afcda9e6c4b571
The turbine meshes (provided by ASTE; https://gitlab.lrz.de/precice/precice2-ref-paper-setup) used for data mapping testing. |
Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
XZ Archive - 1.4 MB -
MD5: f3aad9eac2c568c6f6672a103a946464
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Jun 5, 2023 -
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
Markdown Text - 5.6 KB -
MD5: 39f670a2010544495bb5a3a68ec36478
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Feb 15, 2023
Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/DARUS-2829, DaRUS, V1
This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 benchmark from here and referenced in Köppel et al. 2019 There are a set... |
Python Source Code - 5.3 KB -
MD5: 9b2991df66556aa9485ee028967c2b86
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Python Source Code - 1.6 KB -
MD5: 42a79cc440a6592a635618239dd3b933
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