1 to 10 of 354 Results
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... |
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... |
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... |
Jun 8, 2026 - Usability and Sustainability of Simulation Software
Rajeeshkumar, Sohankumar Thaliyazcha, 2026, "Replication Data for: Towards an API Standard for Coupling Libraries", https://doi.org/10.18419/DARUS-5963, DaRUS, V1
This dataset contains the software components, tutorials and results from the master thesis "Towards an API Standard for Coupling Libraries" by Sohankumar Thaliyazcha Rajeeshkumar. In particular, this includes the source code of the minimal coupler, the preCICE-MUI-bridge, the preCICE tutorials, and the results from the author's runs. The top-level... |
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... |
Jun 2, 2026 - PN 2-6
Range, Jan Peter; Pleiss, Jürgen, 2026, "MD-Models: Human-Readable, Model-Driven Specifications for FAIR Research Data", https://doi.org/10.18419/DARUS-5831, DaRUS, V1
This dataset accompanies MD-Models, a model-driven framework that lets domain experts define structured, machine-readable research data models in plain Markdown. A single human-readable specification serves as the authoritative source, from which technical schemas, programming libraries in five languages, relational and graph database layers, valid... |
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... |
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... |
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... |
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. |
