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1 to 10 of 261 Results

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart) May 20, 2026PN 6 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...
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" May 11, 2026 - PN 3-11 Korn, Viktoria Helena; Pluhackova, Kristyna, 2026, "Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"", https://doi.org/10.18419/DARUS-5682, DaRUS, V2 GROMACS simulation files, input and final structures for CHARMM36m MD simulations including our refined parameters for phosphorylated serine in 3 different protonation states. The directory charmm36-jul22mod.ff contains our refined parameters.
Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification May 8, 2026 - Usability and Sustainability of Simulation Software Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1 This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....
Replication Data for: Data-Parallel PU-RBF Interpolation Apr 21, 2026 - Usability and Sustainability of Simulation Software Schneider, David, 2025, "Replication Data for: Data-Parallel PU-RBF Interpolation", https://doi.org/10.18419/DARUS-5392, DaRUS, V2 This dataset contains software (preCICE and ASTE) as well as setup and result files to reproduce the numerical experiments in Section 5.3 of my dissertation titled "Flexible and Efficient Data Mapping for Simulation of Coupled Problems". For further instructions on how to run the experiments see the README.md of the dataset.
Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software Apr 9, 2026 - Usability and Sustainability of Simulation Software Vinnitchenko, Niklas; Desai, Ishaan; Rodenberg, Benjamin; Hildebrand, Philip; Humbert, Angelika; Uekermann, Benjamin, 2026, "Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software", https://doi.org/10.18419/DARUS-5847, DaRUS, V1 This dataset contains software and result files for the publication: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software in the journal SoftwareX.
Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations 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...
Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB from pathogenic Leptospira" Mar 20, 2026 - PN 3-11 Pluhackova, Kristyna; Hiller, Sebastian; Agustoni, Elia, 2026, "Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB from pathogenic Leptospira"", https://doi.org/10.18419/DARUS-5664, DaRUS, V2 MD data (author K. Pluhackova): Initial coordinate and simulation input files and a coordinate files of the final outputs as well as of the simulation trajectories (protein only) for all-atom MD simulations of LvrB performed using CHARMM36m/TIP4p in GROMACS2023. (pdb, xtc) Simulation data on phosphorylated aspartic acid (residue APP) with parameter...
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...

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart)

May 20, 2026PN 6

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...

Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

May 11, 2026 - PN 3-11

Korn, Viktoria Helena; Pluhackova, Kristyna, 2026, "Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"", https://doi.org/10.18419/DARUS-5682, DaRUS, V2

GROMACS simulation files, input and final structures for CHARMM36m MD simulations including our refined parameters for phosphorylated serine in 3 different protonation states. The directory charmm36-jul22mod.ff contains our refined parameters.

Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification

May 8, 2026 - Usability and Sustainability of Simulation Software

Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1

This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....

Replication Data for: Data-Parallel PU-RBF Interpolation

Apr 21, 2026 - Usability and Sustainability of Simulation Software

Schneider, David, 2025, "Replication Data for: Data-Parallel PU-RBF Interpolation", https://doi.org/10.18419/DARUS-5392, DaRUS, V2

This dataset contains software (preCICE and ASTE) as well as setup and result files to reproduce the numerical experiments in Section 5.3 of my dissertation titled "Flexible and Efficient Data Mapping for Simulation of Coupled Problems". For further instructions on how to run the experiments see the README.md of the dataset.

Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software

Apr 9, 2026 - Usability and Sustainability of Simulation Software

Vinnitchenko, Niklas; Desai, Ishaan; Rodenberg, Benjamin; Hildebrand, Philip; Humbert, Angelika; Uekermann, Benjamin, 2026, "Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software", https://doi.org/10.18419/DARUS-5847, DaRUS, V1

This dataset contains software and result files for the publication: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software in the journal SoftwareX.

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

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...

Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB from pathogenic Leptospira"

Mar 20, 2026 - PN 3-11

Pluhackova, Kristyna; Hiller, Sebastian; Agustoni, Elia, 2026, "Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB from pathogenic Leptospira"", https://doi.org/10.18419/DARUS-5664, DaRUS, V2

MD data (author K. Pluhackova): Initial coordinate and simulation input files and a coordinate files of the final outputs as well as of the simulation trajectories (protein only) for all-atom MD simulations of LvrB performed using CHARMM36m/TIP4p in GROMACS2023. (pdb, xtc) Simulation data on phosphorylated aspartic acid (residue APP) with parameter...

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...

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