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1 to 10 of 84 Results

Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Jul 17, 2026 Herkert, Robin, 2026, "Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation", https://doi.org/10.18419/DARUS-5644, DaRUS, V1 This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation" (2026). It contains the nonparametric variant, in which the surrogate model is trained for one fixed Hamiltonian system and one prescribed macro time step...
Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation Jun 18, 2026 Herkert, Robin, 2026, "Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation", https://doi.org/10.18419/DARUS-6155, DaRUS, V1 This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation" (2026). The data were generated by the provided Python scripts from Hamiltonian test systems and are organized according to the corresp...
Data for "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks" Jun 17, 2026 - Zaumseil group Dataverse Xu, Xuqiang; Fresta, Elisa; Lindenthal, Sebastian; Michel, Edwin; Zaumseil, Jana, 2026, "Data for "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks"", https://doi.org/10.18419/DARUS-5338, DaRUS, V1 This dataset contains all data underlying the figures in the paper "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks" (including the supporting information). Abbreviations used in the headers of the txt-files: Id: drain current Ig: gate (leakage) current gm: transconductance L: channel length W...
Replication Data for: A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio Jun 16, 2026 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries Homs-Pons, Carme; Villota, Yesid; Doreti, Lalith Kumar; Klink, Marian; Lautenschlager, Robin; Göddeke, Dominik; Schulte, Miriam; Röhrle, Oliver, 2026, "Replication Data for: A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio", https://doi.org/10.18419/DARUS-6125, DaRUS, V1 This dataset contains all setup files and instructions to reproduce the experiments in "A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio". In particular, we provide all the input files required to run simulations of muscle contraction using the OpenDiHu and FEBio mechanics solvers. The mechanics solve...
Step 1: Single Heat Plume Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "Step 1: Single Heat Plume", https://doi.org/10.18419/DARUS-5806, DaRUS, V3 This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...
All Steps: Raw Simulation Data Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "All Steps: Raw Simulation Data", https://doi.org/10.18419/DARUS-5920, DaRUS, V2 This dataset contains unprocessed simulation data of modeling heat flow from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapted to fit...
Step 2: Two Interacting Heat Plumes Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "Step 2: Two Interacting Heat Plumes", https://doi.org/10.18419/DARUS-5807, DaRUS, V3 This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...
Step 3: Scaled-up Domain and Interactions of Heat Plumes Jun 3, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "Step 3: Scaled-up Domain and Interactions of Heat Plumes", https://doi.org/10.18419/DARUS-5808, DaRUS, V2 This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...
Smallest Building Blocks Approach to Model Interacting Heat Plumes(Universität Stuttgart) Jun 2, 2026Surrogate models for groundwater flow simulations Datasets (raw and prepared) and trained models for modeling heat plume interactions in a bottom-up approach of small building blocks. It contains data for the publication "Efficient Two-Stage Modeling of Heat Plume Interactions of Geothermal Heat Pumps in Shallow Aquifers Using Convolutional Neural Networks", projects, and student theses that build...
Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics May 22, 2026 - Surrogate models for groundwater flow simulations Baratto, Thomas, 2026, "Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics", https://doi.org/10.18419/DARUS-5815, DaRUS, V3, UNF:6:dsogzGIJc3jbogD4D0lTEA== [fileUNF] Inference package for thermal plume prediction (v1.0.0). Contains pre-trained MLP and randomized neural network models, the ba-predict CLI, the CSV data used to train the models obtained via simulation by Fabian Böttcher, sample input files, and a Dockerfile. CPU-only (no GPU required). Install with: pip install ./code. Full source code (training s...

Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Jul 17, 2026

Herkert, Robin, 2026, "Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation", https://doi.org/10.18419/DARUS-5644, DaRUS, V1

This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation" (2026). It contains the nonparametric variant, in which the surrogate model is trained for one fixed Hamiltonian system and one prescribed macro time step...

Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation

Jun 18, 2026

Herkert, Robin, 2026, "Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation", https://doi.org/10.18419/DARUS-6155, DaRUS, V1

This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation" (2026). The data were generated by the provided Python scripts from Hamiltonian test systems and are organized according to the corresp...

Data for "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks"

Jun 17, 2026 - Zaumseil group Dataverse

Xu, Xuqiang; Fresta, Elisa; Lindenthal, Sebastian; Michel, Edwin; Zaumseil, Jana, 2026, "Data for "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks"", https://doi.org/10.18419/DARUS-5338, DaRUS, V1

This dataset contains all data underlying the figures in the paper "Anion Effects in Electrochemical Transistors with Semiconducting Single-Walled Carbon Nanotube Networks" (including the supporting information). Abbreviations used in the headers of the txt-files: Id: drain current Ig: gate (leakage) current gm: transconductance L: channel length W...

Replication Data for: A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio

Jun 16, 2026 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries

Homs-Pons, Carme; Villota, Yesid; Doreti, Lalith Kumar; Klink, Marian; Lautenschlager, Robin; Göddeke, Dominik; Schulte, Miriam; Röhrle, Oliver, 2026, "Replication Data for: A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio", https://doi.org/10.18419/DARUS-6125, DaRUS, V1

This dataset contains all setup files and instructions to reproduce the experiments in "A partitioned coupling approach for electromechanics simulations of skeletal muscles using FEBio". In particular, we provide all the input files required to run simulations of muscle contraction using the OpenDiHu and FEBio mechanics solvers. The mechanics solve...

Step 1: Single Heat Plume

Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "Step 1: Single Heat Plume", https://doi.org/10.18419/DARUS-5806, DaRUS, V3

This dataset serves as training data for modeling the temperature field emanating from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapt...

All Steps: Raw Simulation Data

Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "All Steps: Raw Simulation Data", https://doi.org/10.18419/DARUS-5920, DaRUS, V2

This dataset contains unprocessed simulation data of modeling heat flow from open-loop groundwater heat pumps. The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions, and extraction rates are adapted to fit...

Step 2: Two Interacting Heat Plumes

Jun 8, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "Step 2: Two Interacting Heat Plumes", https://doi.org/10.18419/DARUS-5807, DaRUS, V3

Step 3: Scaled-up Domain and Interactions of Heat Plumes

Jun 3, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "Step 3: Scaled-up Domain and Interactions of Heat Plumes", https://doi.org/10.18419/DARUS-5808, DaRUS, V2

Smallest Building Blocks Approach to Model Interacting Heat Plumes(Universität Stuttgart)

Jun 2, 2026Surrogate models for groundwater flow simulations

Datasets (raw and prepared) and trained models for modeling heat plume interactions in a bottom-up approach of small building blocks. It contains data for the publication "Efficient Two-Stage Modeling of Heat Plume Interactions of Geothermal Heat Pumps in Shallow Aquifers Using Convolutional Neural Networks", projects, and student theses that build...

Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

May 22, 2026 - Surrogate models for groundwater flow simulations

Baratto, Thomas, 2026, "Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics", https://doi.org/10.18419/DARUS-5815, DaRUS, V3, UNF:6:dsogzGIJc3jbogD4D0lTEA== [fileUNF]

Inference package for thermal plume prediction (v1.0.0). Contains pre-trained MLP and randomized neural network models, the ba-predict CLI, the CSV data used to train the models obtained via simulation by Fabian Böttcher, sample input files, and a Dockerfile. CPU-only (no GPU required). Install with: pip install ./code. Full source code (training s...

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