Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

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1 to 10 of 373 Results

Step 1: Single Heat Plume Sep 17, 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, V5 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 2: Two Interacting Heat Plumes Sep 17, 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, V5 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 Sep 17, 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, V4 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 3b: Scaling Benchmarking Data Sep 17, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "Step 3b: Scaling Benchmarking Data", https://doi.org/10.18419/DARUS-6304, DaRUS, V3 This dataset serves as test data only to benchmark the scaling abilities of models for predicting 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 rea...
Steps 1-3b: Raw Simulation Data Sep 17, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Böttcher, Fabian, 2026, "Steps 1-3b: Raw Simulation Data", https://doi.org/10.18419/DARUS-5920, DaRUS, V4 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...
All Steps: Pretrained Models Sep 14, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling Pelzer, Julia; Kotchourko, Serge, 2026, "All Steps: Pretrained Models", https://doi.org/10.18419/DARUS-6436, DaRUS, V1 Pretrained models for "Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling" This dataset contains the pretrained baseline models, namely CNO, ConvNeXt U-Net, DPOT, LGCNN, scOT, TFNO, and U-Net. A model is trained under a fixed budget on all three training steps in the benchmark and included if training is technically possible under...
Replication data for: Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach Sep 10, 2026 - Usability and Sustainability of Simulation Software Neubauer, Felix, 2026, "Replication data for: Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach", https://doi.org/10.18419/DARUS-6158, DaRUS, V3, UNF:6:UrN7pGlBgFwIikRGgxB/eQ== [fileUNF] This dataset accompanies the paper “Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach” and provides source code snapshots, evaluation inputs and outputs, LLM-based conversion annotations, and derived analyses. It includes the Schema Conversion Orchestrator, a Python/Flask service that discover...
Replication Data for: Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by generalized kernel interpolation Sep 9, 2026 - Projects without PN Affiliation 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, V2 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...
Replication Data for: Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents Sep 7, 2026 - Usability and Sustainability of Simulation Software Chinnikkaramadom Govindan, Giridhar, 2026, "Replication Data for: Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents", https://doi.org/10.18419/DARUS-5964, DaRUS, V1, UNF:6:0+Q1vgk6IYwV/GGvOjFwZg== [fileUNF] This dataset contains files, scripts, and documentation for running benchmarks for the library shacl-bridge, created as part of my master thesis "Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents". For instructions to run the benchmark, see the README.md of the dataset...
Replication Data for: Entropy production of active matter systems as indicator for computing performance Sep 4, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning Egenlauf, Patrick; Kröninger, Hannes A.; Kung, Arnulf; Gaimann, Mario U.; Klopotek, Miriam, 2026, "Replication Data for: Entropy production of active matter systems as indicator for computing performance", https://doi.org/10.18419/DARUS-6343, DaRUS, V1 This dataset contains all relevant data to reproduce the results of the paper titled "Entropy production of active matter systems as indicator for computing performance". The dataset is organized around three main parameter scans: speed-controller force for the driven system, speed-controller force for the undriven system, and driver repulsion forc...

Step 1: Single Heat Plume

Sep 17, 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, V5

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 2: Two Interacting Heat Plumes

Sep 17, 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, V5

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

Sep 17, 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, V4

Step 3b: Scaling Benchmarking Data

Sep 17, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "Step 3b: Scaling Benchmarking Data", https://doi.org/10.18419/DARUS-6304, DaRUS, V3

This dataset serves as test data only to benchmark the scaling abilities of models for predicting 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 rea...

Steps 1-3b: Raw Simulation Data

Sep 17, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Böttcher, Fabian, 2026, "Steps 1-3b: Raw Simulation Data", https://doi.org/10.18419/DARUS-5920, DaRUS, V4

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...

All Steps: Pretrained Models

Sep 14, 2026 - Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling

Pelzer, Julia; Kotchourko, Serge, 2026, "All Steps: Pretrained Models", https://doi.org/10.18419/DARUS-6436, DaRUS, V1

Pretrained models for "Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling" This dataset contains the pretrained baseline models, namely CNO, ConvNeXt U-Net, DPOT, LGCNN, scOT, TFNO, and U-Net. A model is trained under a fixed budget on all three training steps in the benchmark and included if training is technically possible under...

Replication data for: Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach

Sep 10, 2026 - Usability and Sustainability of Simulation Software

Neubauer, Felix, 2026, "Replication data for: Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach", https://doi.org/10.18419/DARUS-6158, DaRUS, V3, UNF:6:UrN7pGlBgFwIikRGgxB/eQ== [fileUNF]

This dataset accompanies the paper “Orchestrating Schema Conversions Across Heterogeneous Schema Languages: A Graph-Based, Black-Box Approach” and provides source code snapshots, evaluation inputs and outputs, LLM-based conversion annotations, and derived analyses. It includes the Schema Conversion Orchestrator, a Python/Flask service that discover...

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

Sep 9, 2026 - Projects without PN Affiliation

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, V2

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...

Replication Data for: Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents

Sep 7, 2026 - Usability and Sustainability of Simulation Software

Chinnikkaramadom Govindan, Giridhar, 2026, "Replication Data for: Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents", https://doi.org/10.18419/DARUS-5964, DaRUS, V1, UNF:6:0+Q1vgk6IYwV/GGvOjFwZg== [fileUNF]

This dataset contains files, scripts, and documentation for running benchmarks for the library shacl-bridge, created as part of my master thesis "Bridging SHACL and JSON Schema: Design, Implementation, and Evaluation of Bidirectional Conversions for JSON and JSON-LD Documents". For instructions to run the benchmark, see the README.md of the dataset...

Replication Data for: Entropy production of active matter systems as indicator for computing performance

Sep 4, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning

Egenlauf, Patrick; Kröninger, Hannes A.; Kung, Arnulf; Gaimann, Mario U.; Klopotek, Miriam, 2026, "Replication Data for: Entropy production of active matter systems as indicator for computing performance", https://doi.org/10.18419/DARUS-6343, DaRUS, V1

This dataset contains all relevant data to reproduce the results of the paper titled "Entropy production of active matter systems as indicator for computing performance". The dataset is organized around three main parameter scans: speed-controller force for the driven system, speed-controller force for the undriven system, and driver repulsion forc...

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