1 to 10 of 54 Results
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... |
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... |
Aug 11, 2026 - PN 6A-3
Schäfer, Moritz; Kästner, Johannes, 2026, "Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-6365, DaRUS, V1
This repository contains the data for our paper on "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches, an... |
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... |
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... |
Jun 19, 2026 - PN 6-15
Gaimann, Mario U.; Klopotek, Miriam, 2025, "Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4805, DaRUS, V3, UNF:6:C1gH8zVD6YKFF/O3ZhSaSA== [fileUNF]
This repository contains raw and post-processed replication data for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025). The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external... |
Jun 19, 2026 - PN 6-15
Gaimann, Mario U.; Klopotek, Miriam, 2025, "Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4806, DaRUS, V3
This dataset contains supplementary videos for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025) (to be published). The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external for... |
May 20, 2026
We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh... |
Apr 7, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning
Egenlauf, Patrick; Březinová, Iva; Andergassen, Sabine; Klopotek, Miriam, 2026, "Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations", https://doi.org/10.18419/DARUS-5613, DaRUS, V3
This dataset contains all relevant data to reproduce the results of the paper titled "Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations". It contains the exact time series data of the two-particle reduced density matrix (2RDM) for each parameter configuration of the parameter scan, with... |
Mar 10, 2026 - PN 6A-3
Schäfer, Moritz; Segreto, Nico; Zills, Fabian; Holm, Christian; Kästner, Johannes, 2026, "Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-5007, DaRUS, V1
This repository contains the data for our paper on "Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches and a more detailed REA... |
