Stuttgart Center for Simulation Science (SC SimTech)

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31 to 40 of 375 Results

MD-Models: Human-Readable, Model-Driven Specifications for FAIR Research Data 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...
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; Böttcher, Fabian, 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...
Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart) 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...
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" 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.
Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification May 8, 2026 - Usability and Sustainability of Simulation Software Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1 This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....
Data for: "3D Digital Light Processing of Redox-Active Polymers for Electrochemical Applications" Apr 30, 2026 - Blasco group Dataverse Delavier, Christian; Bechtold, Svenja, 2026, "Data for: "3D Digital Light Processing of Redox-Active Polymers for Electrochemical Applications"", https://doi.org/10.18419/DARUS-5532, DaRUS, V1 This dataset contains the material characterization data for the related publication as well as pictures, figures and synthetic procedure information. The data is provided in two separate folders, with the folder titled Daten_DaRUS containing the data generated at Heidelberg University and the folder SE-EC Daten_DaRUS containing the data generated...
Replication Data for: Data-Parallel PU-RBF Interpolation Apr 21, 2026 - Usability and Sustainability of Simulation Software Schneider, David, 2025, "Replication Data for: Data-Parallel PU-RBF Interpolation", https://doi.org/10.18419/DARUS-5392, DaRUS, V2 This dataset contains software (preCICE and ASTE) as well as setup and result files to reproduce the numerical experiments in Section 5.3 of my dissertation titled "Flexible and Efficient Data Mapping for Simulation of Coupled Problems". For further instructions on how to run the experiments see the README.md of the dataset.
Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling(Universität Stuttgart) Apr 14, 2026Surrogate models for groundwater flow simulations This dataverse contains the datasets for all steps of our stepwise benchmark based on input data from the region of Munich. The first step starts with a single heat pump in a heterogeneous 2D subsurface aquifer with seasonal operational pump parameters. The second step contains two potentially interacting heat plumes and fewer data points due to th...
Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software Apr 9, 2026 - Usability and Sustainability of Simulation Software Vinnitchenko, Niklas; Desai, Ishaan; Rodenberg, Benjamin; Hildebrand, Philip; Humbert, Angelika; Uekermann, Benjamin, 2026, "Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software", https://doi.org/10.18419/DARUS-5847, DaRUS, V1 This dataset contains software and result files for the publication: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software in the journal SoftwareX.

MD-Models: Human-Readable, Model-Driven Specifications for FAIR Research Data

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...

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; Böttcher, Fabian, 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...

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart)

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...

Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

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.

Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification

May 8, 2026 - Usability and Sustainability of Simulation Software

Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1

This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....

Data for: "3D Digital Light Processing of Redox-Active Polymers for Electrochemical Applications"

Apr 30, 2026 - Blasco group Dataverse

Delavier, Christian; Bechtold, Svenja, 2026, "Data for: "3D Digital Light Processing of Redox-Active Polymers for Electrochemical Applications"", https://doi.org/10.18419/DARUS-5532, DaRUS, V1

This dataset contains the material characterization data for the related publication as well as pictures, figures and synthetic procedure information. The data is provided in two separate folders, with the folder titled Daten_DaRUS containing the data generated at Heidelberg University and the folder SE-EC Daten_DaRUS containing the data generated...

Replication Data for: Data-Parallel PU-RBF Interpolation

Apr 21, 2026 - Usability and Sustainability of Simulation Software

Schneider, David, 2025, "Replication Data for: Data-Parallel PU-RBF Interpolation", https://doi.org/10.18419/DARUS-5392, DaRUS, V2

This dataset contains software (preCICE and ASTE) as well as setup and result files to reproduce the numerical experiments in Section 5.3 of my dissertation titled "Flexible and Efficient Data Mapping for Simulation of Coupled Problems". For further instructions on how to run the experiments see the README.md of the dataset.

Stepwise Benchmarking: Data-Scarce Groundwater Heat Pumps' Modeling(Universität Stuttgart)

Apr 14, 2026Surrogate models for groundwater flow simulations

This dataverse contains the datasets for all steps of our stepwise benchmark based on input data from the region of Munich. The first step starts with a single heat pump in a heterogeneous 2D subsurface aquifer with seasonal operational pump parameters. The second step contains two potentially interacting heat plumes and fewer data points due to th...

Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software

Apr 9, 2026 - Usability and Sustainability of Simulation Software

Vinnitchenko, Niklas; Desai, Ishaan; Rodenberg, Benjamin; Hildebrand, Philip; Humbert, Angelika; Uekermann, Benjamin, 2026, "Replication Data for: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software", https://doi.org/10.18419/DARUS-5847, DaRUS, V1

This dataset contains software and result files for the publication: Version 1.0.0 - FEniCSx-preCICE: Coupling FEniCSx to other simulation software in the journal SoftwareX.

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