PN 6

SimTech EXC 2075 Project Network 6 "Machine learning for simulation"

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21 to 30 of 4,915 Results

erbs_data.tar.part.009 Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials Unknown - 5.3 GB - MD5: 25d778b9b7fca5294e4024de8126cc8e Data split archive part erbs_data.tar.part.009
erbs_inputs.tar.zst Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials Unknown - 95.7 MB - MD5: cae2422ed3ec42ab536af5a45d8fa788 Data complete repository accompanying the manuscript
README.md Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials Markdown Text - 894 B - MD5: c80d70e8158e13586e05f61b06b2551a Data complete repository accompanying the manuscript
Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Jul 22, 2026 - Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing Weinmann, Max; Klopotek, Miriam, 2026, "Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model", https://doi.org/10.18419/DARUS-6128, DaRUS, V1 This dataset contains all relevant data to reproduce the results of the paper titled "Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model" and supplementary figures. "code.tar.xz" contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code incl...
checkpoints.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 515.9 GB - MD5: d24de0e5275b5f794548bab513cff69f CheckpointsDataModelParameters Contains the resulting model checkpoints, including the used training configuration parameters and some metrics computed based on the datasets. The model checkpoints where saved during training with different hyper-parameters.
code.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 133.4 KB - MD5: f8710213b5347894e074dcfd86085fe7 Code Contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code including instructions on how to reproduce the data can be found in the file README.md.
figures.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 276.8 MB - MD5: c304a7b7f425cea51012c45b2294e2fe DataFigures Contains supplementary figures for the paper. These contain figures for all the different hyperparameters and datasets which are not shown in the paper.
ising_data.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 21.1 MB - MD5: 846676e1c3d289bc9db1542c22a54b76 Data Contains the resulting datasets. The datasets where generated with Markov-Chain-Monte-Carlo based on Glauber Dynamics of the Ising model and used for training and validation.
Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling Jun 22, 2026 - PN 6A-4 Álvarez Chaves, Manuel, 2026, "Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling", https://doi.org/10.18419/DARUS-5118, DaRUS, V1 This is repository contains code and archived results for the paper "A variational approach at uncertainty estimation in rainfall-runoff modeling" submitted for review to the ML:Earth journal. Instructions for running the training or evaluation workflows can be found in the README.md file located within the scripts/ folder. This dataset primarily c...
baseconceptualmodel.py Jun 22, 2026 - Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling Python Source Code - 7.2 KB - MD5: 30a744a2f248efbcc47e69e826f15588

erbs_data.tar.part.009

Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials

Unknown - 5.3 GB -

Data

split archive part erbs_data.tar.part.009

erbs_inputs.tar.zst

Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials

Unknown - 95.7 MB -

Data

complete repository accompanying the manuscript

README.md

Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials

Markdown Text - 894 B -

Data

complete repository accompanying the manuscript

Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

Jul 22, 2026 - Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing

Weinmann, Max; Klopotek, Miriam, 2026, "Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model", https://doi.org/10.18419/DARUS-6128, DaRUS, V1

This dataset contains all relevant data to reproduce the results of the paper titled "Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model" and supplementary figures. "code.tar.xz" contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code incl...

checkpoints.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 515.9 GB -

CheckpointsDataModelParameters

Contains the resulting model checkpoints, including the used training configuration parameters and some metrics computed based on the datasets. The model checkpoints where saved during training with different hyper-parameters.

code.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 133.4 KB -

Code

Contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code including instructions on how to reproduce the data can be found in the file README.md.

figures.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 276.8 MB -

DataFigures

Contains supplementary figures for the paper. These contain figures for all the different hyperparameters and datasets which are not shown in the paper.

ising_data.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 21.1 MB -

Data

Contains the resulting datasets. The datasets where generated with Markov-Chain-Monte-Carlo based on Glauber Dynamics of the Ising model and used for training and validation.

Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling

Jun 22, 2026 - PN 6A-4

Álvarez Chaves, Manuel, 2026, "Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling", https://doi.org/10.18419/DARUS-5118, DaRUS, V1

This is repository contains code and archived results for the paper "A variational approach at uncertainty estimation in rainfall-runoff modeling" submitted for review to the ML:Earth journal. Instructions for running the training or evaluation workflows can be found in the README.md file located within the scripts/ folder. This dataset primarily c...

baseconceptualmodel.py

Jun 22, 2026 - Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling

Python Source Code - 7.2 KB -

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