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Keyword Term: Machine Learning

1 to 10 of 31 Results

Supplementary Videos for: Robustly optimal dynamics for active matter reservoir computing (Gaimann and Klopotek, 2025) May 12, 2025 - 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, V1 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) May 12, 2025 - 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, V1, UNF:6:JR70RsK1jeEUWlriMxeIpw== [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...
Code for using and training spray segmentation models Feb 27, 2025 - Spray Segmentation Jose, Basil; Hampp, Fabian, 2025, "Code for using and training spray segmentation models", https://doi.org/10.18419/DARUS-4739, DaRUS, V1 This dataset contains the necessary code for using our spray segmentation model used in the paper, ML-based semantic segmentation for quantitative spray atomization description. See README for more information.
Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Jan 22, 2025 - Materials Design Zhang, Xi, 2024, "Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten", https://doi.org/10.18419/DARUS-4564, DaRUS, V2 The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten": DFT input files: INCAR, KPOINTS. All POSCAR files for DFT and thermodynamic integration Moment tensor potential (MTP) file Training dataset for MTP All Hessian Matri...
Data related to Panzer: A Machine Learning Based Approach to Analyze Supersecondary Structures of Proteins Nov 27, 2024 - Institute of Biochemistry Panzer, Tim, 2024, "Data related to Panzer: A Machine Learning Based Approach to Analyze Supersecondary Structures of Proteins", https://doi.org/10.18419/DARUS-4576, DaRUS, V1 This entry contains the data used to implement the bachelor thesis. It was investigated how embeddings can be used to analyze supersecondary structures. Abstract of the thesis: This thesis analyzes the behavior of supersecondary structures in the context of embeddings. For this purpose, data from the Protein Topology Graph Library was provided with...
Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Oct 29, 2024 - Materials Design Ou, Yongliang; Ikeda, Yuji; Scholz, Lena; Divinski, Sergiy; Fritzen, Felix; Grabowski, Blazej, 2024, "Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials", https://doi.org/10.18419/DARUS-4510, DaRUS, V1 The data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and gr...
Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking Sep 25, 2024 - 2023_ICM_RoboCable Zürn, Manuel, 2024, "Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking", https://doi.org/10.18419/DARUS-4236, DaRUS, V1 Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking. 4K Images from 3 Camera Perspectives including Annotation data for Reproduction of the Learning for the ML Model. It includes the tensorboard logfile representing the quantitative loss during training, the qualitative spline prediction during training and the final trained model we...
Floor Type Detection Dataset Aug 12, 2024 - Robot Dog Go1 Edu Kamm, Simon; Eißen, Dominik; Jazdi, Nasser; Weyrich, Michael, 2024, "Floor Type Detection Dataset", https://doi.org/10.18419/DARUS-4353, DaRUS, V2 Dataset for Floor Type Detection of the Robot Dog Unitree Go1 Edu. Details can be found in the seperate report. !!Privacy Statement!! This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us and we will immed...
Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning Aug 2, 2024 - Holm group Tovey, Samuel; Lohrmann, Christoph; Holm, Christian, 2024, "Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning", https://doi.org/10.18419/DARUS-4431, DaRUS, V1 Scripts used in the experiments and analysis presented in the paper.
Code for training and using the droplet segmentation models Jul 2, 2024 - Spray Segmentation Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the droplet segmentation models", https://doi.org/10.18419/DARUS-4147, DaRUS, V1 This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md.

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

May 12, 2025 - 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, V1

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)

May 12, 2025 - 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, V1, UNF:6:JR70RsK1jeEUWlriMxeIpw== [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...

Code for using and training spray segmentation models

Feb 27, 2025 - Spray Segmentation

Jose, Basil; Hampp, Fabian, 2025, "Code for using and training spray segmentation models", https://doi.org/10.18419/DARUS-4739, DaRUS, V1

This dataset contains the necessary code for using our spray segmentation model used in the paper, ML-based semantic segmentation for quantitative spray atomization description. See README for more information.

Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

Jan 22, 2025 - Materials Design

Zhang, Xi, 2024, "Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten", https://doi.org/10.18419/DARUS-4564, DaRUS, V2

The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten": DFT input files: INCAR, KPOINTS. All POSCAR files for DFT and thermodynamic integration Moment tensor potential (MTP) file Training dataset for MTP All Hessian Matri...

Data related to Panzer: A Machine Learning Based Approach to Analyze Supersecondary Structures of Proteins

Nov 27, 2024 - Institute of Biochemistry

Panzer, Tim, 2024, "Data related to Panzer: A Machine Learning Based Approach to Analyze Supersecondary Structures of Proteins", https://doi.org/10.18419/DARUS-4576, DaRUS, V1

This entry contains the data used to implement the bachelor thesis. It was investigated how embeddings can be used to analyze supersecondary structures. Abstract of the thesis: This thesis analyzes the behavior of supersecondary structures in the context of embeddings. For this purpose, data from the Protein Topology Graph Library was provided with...

Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Oct 29, 2024 - Materials Design

Ou, Yongliang; Ikeda, Yuji; Scholz, Lena; Divinski, Sergiy; Fritzen, Felix; Grabowski, Blazej, 2024, "Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials", https://doi.org/10.18419/DARUS-4510, DaRUS, V1

The data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and gr...

Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking

Sep 25, 2024 - 2023_ICM_RoboCable

Zürn, Manuel, 2024, "Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking", https://doi.org/10.18419/DARUS-4236, DaRUS, V1

Replication Data for: SCAP 2024 Robotic Wiring Harness Bin Picking. 4K Images from 3 Camera Perspectives including Annotation data for Reproduction of the Learning for the ML Model. It includes the tensorboard logfile representing the quantitative loss during training, the qualitative spline prediction during training and the final trained model we...

Floor Type Detection Dataset

Aug 12, 2024 - Robot Dog Go1 Edu

Kamm, Simon; Eißen, Dominik; Jazdi, Nasser; Weyrich, Michael, 2024, "Floor Type Detection Dataset", https://doi.org/10.18419/DARUS-4353, DaRUS, V2

Dataset for Floor Type Detection of the Robot Dog Unitree Go1 Edu. Details can be found in the seperate report. !!Privacy Statement!! This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us and we will immed...

Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning

Aug 2, 2024 - Holm group

Tovey, Samuel; Lohrmann, Christoph; Holm, Christian, 2024, "Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning", https://doi.org/10.18419/DARUS-4431, DaRUS, V1

Scripts used in the experiments and analysis presented in the paper.

Code for training and using the droplet segmentation models

Jul 2, 2024 - Spray Segmentation

Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the droplet segmentation models", https://doi.org/10.18419/DARUS-4147, DaRUS, V1

This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md.

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