PN 6

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

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1 to 10 of 48 Results

Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations Mar 23, 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, V2 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...
Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) Mar 11, 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, V2, 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...
Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) Mar 11, 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, V2 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...
Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials 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...
Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025) Feb 18, 2026 - PN 6-15 Gaimann, Mario U.; Klopotek, Miriam, 2025, "Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4619, DaRUS, V2 This dataset contains supplementary videos for the publication "Robustly optimal dynamics for active matter reservoir computing" (Gaimann and Klopotek, 2025) The videos show active matter systems (swarms) driven by an external force. These swarm systems can be used to predict the future trajectory of the external driving force using reservoir compu...
Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025) Feb 18, 2026 - PN 6-15 Gaimann, Mario U.; Klopotek, Miriam, 2025, "Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4620, DaRUS, V2, UNF:6:IH6gqmMQzmJFTsouARfwHQ== [fileUNF] This repository contains raw and post-processed replication data for the publication "Robustly optimal dynamics 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 force. These simulations se...
Coarse-grained non-equilibrium dynamics with generative machine learning(Universität Stuttgart) Dec 15, 2025
Replication Data for: "BSP k-Means" Dec 11, 2025 - PN 6-4 Künzel, Sebastian; Weiskopf, Daniel, 2025, "Replication Data for: "BSP k-Means"", https://doi.org/10.18419/DARUS-5575, DaRUS, V1 This dataset contains reference implementations in C++ and CUDA for the algorithm presented in the paper "BSP k-Means". The purpose of the data is to allow replication of the reported results.
Supplemental Material for "Potentially Visible Set Generation with the Disocclusion Buffer" Oct 29, 2025 - PN 6-4 Künzel, Sebastian; Geringer, Sergej; Quang Ngo, Quynh; Voglreiter, Philip; Weiskopf, Daniel; Schmalstieg, Dieter, 2025, "Supplemental Material for "Potentially Visible Set Generation with the Disocclusion Buffer"", https://doi.org/10.18419/DARUS-5385, DaRUS, V1 Supplemental material for our paper "Potentially Visible Set Generation with the Disocclusion Buffer": A video showcasing our architecture and example visualizations The source archive to replicate the results reported in our paper
Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples May 28, 2025 - PN 6-8 (II) Bauer, Ruben; Evers, Marina; Quang Ngo, Quynh; Reina, Guido; Frey, Steffen; Sedlmair, Michael, 2025, "Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples", https://doi.org/10.18419/DARUS-4930, DaRUS, V1, UNF:6:3NivtJY91pyT7tXMB/YrJQ== [fileUNF] This dataset contains the data and code for the publication: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples. Code Repository A dynamic version of the code repository can be found at https://github.com/rbnbr/VoroParaSense. The version presented in this dataset corresponds to the version used in the corresponding pape...

Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations

Mar 23, 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, V2

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...

Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

Mar 11, 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, V2, 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...

Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

Mar 11, 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, V2

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...

Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials

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...

Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)

Feb 18, 2026 - PN 6-15

Gaimann, Mario U.; Klopotek, Miriam, 2025, "Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4619, DaRUS, V2

This dataset contains supplementary videos for the publication "Robustly optimal dynamics for active matter reservoir computing" (Gaimann and Klopotek, 2025) The videos show active matter systems (swarms) driven by an external force. These swarm systems can be used to predict the future trajectory of the external driving force using reservoir compu...

Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)

Feb 18, 2026 - PN 6-15

Gaimann, Mario U.; Klopotek, Miriam, 2025, "Replication Data for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4620, DaRUS, V2, UNF:6:IH6gqmMQzmJFTsouARfwHQ== [fileUNF]

This repository contains raw and post-processed replication data for the publication "Robustly optimal dynamics 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 force. These simulations se...

Coarse-grained non-equilibrium dynamics with generative machine learning(Universität Stuttgart)

Dec 15, 2025

Replication Data for: "BSP k-Means"

Dec 11, 2025 - PN 6-4

Künzel, Sebastian; Weiskopf, Daniel, 2025, "Replication Data for: "BSP k-Means"", https://doi.org/10.18419/DARUS-5575, DaRUS, V1

This dataset contains reference implementations in C++ and CUDA for the algorithm presented in the paper "BSP k-Means". The purpose of the data is to allow replication of the reported results.

Supplemental Material for "Potentially Visible Set Generation with the Disocclusion Buffer"

Oct 29, 2025 - PN 6-4

Künzel, Sebastian; Geringer, Sergej; Quang Ngo, Quynh; Voglreiter, Philip; Weiskopf, Daniel; Schmalstieg, Dieter, 2025, "Supplemental Material for "Potentially Visible Set Generation with the Disocclusion Buffer"", https://doi.org/10.18419/DARUS-5385, DaRUS, V1

Supplemental material for our paper "Potentially Visible Set Generation with the Disocclusion Buffer": A video showcasing our architecture and example visualizations The source archive to replicate the results reported in our paper

Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples

May 28, 2025 - PN 6-8 (II)

Bauer, Ruben; Evers, Marina; Quang Ngo, Quynh; Reina, Guido; Frey, Steffen; Sedlmair, Michael, 2025, "Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples", https://doi.org/10.18419/DARUS-4930, DaRUS, V1, UNF:6:3NivtJY91pyT7tXMB/YrJQ== [fileUNF]

This dataset contains the data and code for the publication: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples. Code Repository A dynamic version of the code repository can be found at https://github.com/rbnbr/VoroParaSense. The version presented in this dataset corresponds to the version used in the corresponding pape...

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