591 to 600 of 25,724 Results
Unknown - 740 B -
MD5: b4fd1902771a10e369e216c252f08cff
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JSON - 236 B -
MD5: a2472bbba9813569534848668c5124fd
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JSON - 78 B -
MD5: 2fafea0ef1b0820ca21f6ee16e17a0d9
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JSON - 121 B -
MD5: 4229821642d49aeeb356ce9955abe027
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JSON - 88 B -
MD5: b030fc1e2c660b1ab02b4b5e441e1b6e
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TAR Archive - 2.6 MB -
MD5: 0ce162a062034b42e51ed8eca0f0e93c
Source code of shacl-bridge v1.7.1 |
Sep 4, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning
Egenlauf, Patrick; Kröninger, Hannes A.; Kung, Arnulf; Gaimann, Mario U.; Klopotek, Miriam, 2026, "Replication Data for: Entropy production of active matter systems as indicator for computing performance", https://doi.org/10.18419/DARUS-6343, DaRUS, V1
This dataset contains all relevant data to reproduce the results of the paper titled "Entropy production of active matter systems as indicator for computing performance". The dataset is organized around three main parameter scans: speed-controller force for the driven system, speed-controller force for the undriven system, and driver repulsion forc... |
Sep 4, 2026 -
Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 227.3 GB -
MD5: 48235e427754d39c38d8e85bdf30dd53
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Sep 4, 2026 -
Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 842.6 GB -
MD5: ac52d5125abe68da39aaab1395902e7b
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Sep 4, 2026 -
Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 863.5 GB -
MD5: 8217dd3fd842e2097bed73ff24c11652
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