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Keyword Term: Machine Learning

1 to 10 of 24 Results

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...
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 spray segmentation models Jul 2, 2024 - Spray Segmentation Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the spray 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.
Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data Jun 6, 2024 - Projects without PN Affiliation Herkert, Robin, 2024, "Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data", https://doi.org/10.18419/darus-4227, DaRUS, V1 This dataset includes the code to reproduce the results from the paper titled "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". In this paper we address the challenging application of 3D pore scale reactive flow under va...
Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives Mar 21, 2024 - 2019_DFG_ZRA_Gleichlaufkompensation Steinle, Lukas, 2024, "Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives", https://doi.org/10.18419/darus-3759, DaRUS, V1, UNF:6:yyyPRR4Wz8WOv1lj5nOToA== [fileUNF] This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives". Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over lo...
Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference Mar 14, 2024 - PN 2-7 Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/darus-4093, DaRUS, V1 This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.
Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom Mar 8, 2024 - Materials Design Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/darus-3891, DaRUS, V1 Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figure...
Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes Feb 16, 2024 - PN3-5 Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/darus-3881, DaRUS, V1 The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the con...
PDEBench Datasets Feb 13, 2024 - SciML PDE Benchmark Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/darus-2986, DaRUS, V8 This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This...
Replication Data for: Constraint-aware neural networks for Riemann problems Jan 11, 2024 - Projects without PN Affiliation Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/darus-3869, DaRUS, V1 Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in...

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...

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 spray segmentation models

Jul 2, 2024 - Spray Segmentation

Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the spray 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.

Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Jun 6, 2024 - Projects without PN Affiliation

Herkert, Robin, 2024, "Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data", https://doi.org/10.18419/darus-4227, DaRUS, V1

This dataset includes the code to reproduce the results from the paper titled "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". In this paper we address the challenging application of 3D pore scale reactive flow under va...

Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives

Mar 21, 2024 - 2019_DFG_ZRA_Gleichlaufkompensation

Steinle, Lukas, 2024, "Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives", https://doi.org/10.18419/darus-3759, DaRUS, V1, UNF:6:yyyPRR4Wz8WOv1lj5nOToA== [fileUNF]

This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives". Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over lo...

Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Mar 14, 2024 - PN 2-7

Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/darus-4093, DaRUS, V1

This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md.

Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom

Mar 8, 2024 - Materials Design

Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/darus-3891, DaRUS, V1

Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figure...

Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes

Feb 16, 2024 - PN3-5

Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/darus-3881, DaRUS, V1

The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the con...

PDEBench Datasets

Feb 13, 2024 - SciML PDE Benchmark

Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/darus-2986, DaRUS, V8

This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This...

Replication Data for: Constraint-aware neural networks for Riemann problems

Jan 11, 2024 - Projects without PN Affiliation

Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/darus-3869, DaRUS, V1

Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in...

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