SimTech EXC 2075 Project Network 6 "Machine learning for simulation"
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1 to 10 of 4,903 Results
Replication Data for: Entropy production of active matter systems as indicator for computing performance
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...
attractors.tar.xz
Sep 4, 2026 - Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 227.3 GB - MD5: 48235e427754d39c38d8e85bdf30dd53
driver-repulsion-scan.tar.xz
Sep 4, 2026 - Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 842.6 GB - MD5: ac52d5125abe68da39aaab1395902e7b
speed-controller-scan_driven.tar.xz
Sep 4, 2026 - Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 863.5 GB - MD5: 8217dd3fd842e2097bed73ff24c11652
speed-controller-scan_undriven-burnin.tar.xz
Sep 4, 2026 - Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 79.4 GB - MD5: c38f0700a9412060cbf6bf86ef989948
speed-controller-scan_undriven.tar.xz
Sep 4, 2026 - Replication Data for: Entropy production of active matter systems as indicator for computing performance
XZ Archive - 3.9 TB - MD5: 09cc03fbbafa9fafc9a103ec758aa7ef
Replication Data for: How to Train a Shallow Ensemble
Aug 12, 2026 - PN 6A-3
Schäfer, Moritz; Kellner, Matthias; Kästner, Johannes; Ceriotti, Michele, 2026, "Replication Data for: How to Train a Shallow Ensemble", https://doi.org/10.18419/DARUS-6366, DaRUS, V1
This repository accompanies our paper on "How to Train a Shallow Ensemble". It includes workflows and input files needed to reproduce the results. Most of the experiments are contained in the directory "0_convergence_nll_training". There is a separate directory for the experiments performed for the first revision of the publication and for the repe...
train_atoms_bmim_nod3.extxyz
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 51.1 MB - MD5: a82b6a68bedf20e5c03657fb0e99ad8f
Data
complete repository accompanying the manuscript
uncertainty_project.tar.zst
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 1.2 GB - MD5: e6adb5cfd217cf5536bf95552c111bec
Data
complete repository accompanying the manuscript
val_atoms_bmim_nod3.extxyz
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 3.0 MB - MD5: 9e0f05173fe02f700cafc1e2a8ebd988
Data
complete repository accompanying the manuscript
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