Institute of Applied Analysis and Numerical Simulation

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1,441 to 1,450 of 1,480 Results

util_optimizer.cpython-312.pyc Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Unknown - 2.8 KB - MD5: e8f569e09e5b96f40fdb47ded9368a8a
util_optimizer.cpython-313.pyc Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Unknown - 2.6 KB - MD5: cc640e4bf9747566cd80fa9c0199f041
util_optimizer.py Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Python Source Code - 2.1 KB - MD5: d810ff12c3161a63c633087a0b95db5a
util_plots.py Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Python Source Code - 4.5 KB - MD5: 6ad612f0ce02b9f15b4534301f35b3f3
VKOGA_ML.cpython-312.pyc Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Unknown - 54.4 KB - MD5: 544c3388d43724f30b87e695d96bd337
VKOGA_ML.cpython-313.pyc Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Unknown - 54.1 KB - MD5: 0965a5c05ea4ba5937b34e7e60174680
VKOGA_ML.py Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods Python Source Code - 44.8 KB - MD5: 7163e01d441f412bc1091bd9b8644a90
Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking" Dec 15, 2025 Kemlin, Gaspard; Carvalho Corso, Thiago; Stamm, Benjamin; Melcher, Christof, 2025, "Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking"", https://doi.org/10.18419/DARUS-5604, DaRUS, V1 Data for reproducibility of the numerical simulations of the research paper "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking". The .zip file contains everything needed to generate the plots shown in the paper, as well as the code to run your own simulations with the numerical method presented in the paper. You just have to e...
vortex_tracking_mag_paper_DaRUS.zip Dec 15, 2025 - Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking" ZIP Archive - 2.7 GB - MD5: 570c02e4b2a2b3bfc0a3987e8303c050
Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix Nov 3, 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, V2 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.

util_optimizer.cpython-312.pyc

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Unknown - 2.8 KB -

util_optimizer.cpython-313.pyc

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Unknown - 2.6 KB -

util_optimizer.py

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Python Source Code - 2.1 KB -

util_plots.py

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Python Source Code - 4.5 KB -

VKOGA_ML.cpython-312.pyc

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Unknown - 54.4 KB -

VKOGA_ML.cpython-313.pyc

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Unknown - 54.1 KB -

VKOGA_ML.py

Mar 4, 2026 - Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods

Python Source Code - 44.8 KB -

Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking"

Dec 15, 2025

Kemlin, Gaspard; Carvalho Corso, Thiago; Stamm, Benjamin; Melcher, Christof, 2025, "Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking"", https://doi.org/10.18419/DARUS-5604, DaRUS, V1

Data for reproducibility of the numerical simulations of the research paper "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking". The .zip file contains everything needed to generate the plots shown in the paper, as well as the code to run your own simulations with the numerical method presented in the paper. You just have to e...

vortex_tracking_mag_paper_DaRUS.zip

Dec 15, 2025 - Replication Data for: "Simulation of the magnetic Ginzburg-Landau equation via vortex tracking"

ZIP Archive - 2.7 GB -

Replication Data for: A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Nov 3, 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, V2

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.

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