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1 to 10 of 28 Results
Software for "A generalized Riemann problem solver for a hyperbolic model governing two-layer thin film flow"
May 20, 2025
Barthwal, Rahul; Rohde, Christian; Wang, Yue, 2025, "Software for "A generalized Riemann problem solver for a hyperbolic model governing two-layer thin film flow"", https://doi.org/10.18419/DARUS-5052, DaRUS, V1
In this project, we developed a Generalized Riemann solver for a hyperbolic model which governs first order dynamics of two-layer thin film flow under the influence of an anti-surfactant. The solver is a one-dimensional spatial-temporal coupled second-order finite-volume solver and can be utilized for further development.
GRP_Thin_Film_main.tar.xz
May 20, 2025 - Software for "A generalized Riemann problem solver for a hyperbolic model governing two-layer thin film flow"
XZ Archive - 106.3 MB - MD5: db780a0fa3a8dfb7f3418f105e59d439
README.md
May 20, 2025 - Software for "A generalized Riemann problem solver for a hyperbolic model governing two-layer thin film flow"
Markdown Text - 1.9 KB - MD5: 5c87ab5a08fb75e721d568595586a9c7
Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix
Mar 24, 2025
Zhang, Liwei; Mazzeo, Patrizia; Nottoli, Michele; Cignoni, Edoardo; Cupellini, Lorenzo; Stamm, Benjamin, 2025, "Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix", https://doi.org/10.18419/DARUS-4902, DaRUS, V1
This archive for reproducibility contains the datasets used to train the models, a small script to read the datasets, and the finest model. More information can be found in the README.md.
learning_densities.tar.gz
Mar 24, 2025 - Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix
Gzip Archive - 37.5 GB - MD5: 15a906244ec76d846dc661664dc4235c
README.md
Mar 24, 2025 - Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix
Plain Text - 1.3 KB - MD5: aecb0f5b62dbc76bae23f492fa8d4c96
Replication Data for: Grassmann Extrapolation for Accelerating Geometry Optimization
Oct 9, 2024
Askarpour, Zahra; Nottoli, Michele; Stamm, Benjamin, 2024, "Replication Data for: Grassmann Extrapolation for Accelerating Geometry Optimization", https://doi.org/10.18419/DARUS-4470, DaRUS, V1
Data for reproducibility of the numerical simulations of the research paper: Grassmann Extrapolation for Accelerating Geometry Optimization
benchmark_implementation.tar.gz
Oct 9, 2024 - Replication Data for: Grassmann Extrapolation for Accelerating Geometry Optimization
Gzip Archive - 94.2 MB - MD5: 7c744146fb39365ea1d6a2b4c160d27d
benchmark.tar.gz
Oct 9, 2024 - Replication Data for: Grassmann Extrapolation for Accelerating Geometry Optimization
Gzip Archive - 160.3 KB - MD5: eb06c2bcd9ffda142b47b477f5e0349d
calibration_implementation.tar.gz
Oct 9, 2024 - Replication Data for: Grassmann Extrapolation for Accelerating Geometry Optimization
Gzip Archive - 134.2 MB - MD5: 29ec29c3bc5ac7a65e8ae3eb0c5abcbb
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