1 to 10 of 300 Results
Jul 24, 2025 - demoa
Hammer, Maria; Riede, Julia Maria; Meszaros-Beller, Laura; Schmitt, Syn, 2022, "gspine: A Human Spine Model Built Using Literature Data", https://doi.org/10.18419/DARUS-2814, DaRUS, V5
A fully articulating human spine model parametrised using generic literature data for the geometry of the skeleton including attachment points for ligaments and muscles. The model is prepared to run muscle-driven simulations using a simple biological motor control model. The file contains an archive including all relevant data to run the simulation... |
Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/DARUS-2550, DaRUS, V7
For more information, such as installation, requirements and user guide, please see the demoa manual. The development of this package was supported by “Deutsche Forschungsgemeinschaft” (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016. |
Neubauer, Felix, 2025, "Replication Data for: AI-assisted JSON Schema Creation and Mapping", https://doi.org/10.18419/DARUS-5157, DaRUS, V1, UNF:6:cu/nriFjeIsGtYAoB7K7fw== [fileUNF]
The steps and sources of the application example from the paper + the JSONata instructions for the LLM. Recommended: download all files and open the README.md with a markdown editor/viewer. The README.md document shows the steps performed in the application example and also corresponding screenshots and input and output documents. The screenshots a... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2024, "Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 3 + 1 Data Points", https://doi.org/10.18419/DARUS-4156, DaRUS, V2
This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 3 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points", https://doi.org/10.18419/DARUS-5065, DaRUS, V1
This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 4 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia; Verburg, Corné, 2025, "Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points", https://doi.org/10.18419/DARUS-5081, DaRUS, V1
Models are trained with [git: DDUNet] on 101 data points (dp). Both, vanilla UNet and DDU-Net, can be applied directly end-to-end. For inference follow the guidelines of Heat Plume Prediction to prepare raw data, then apply the models as described in [git: DDUNet]. Based on raw data from https://doi.org/10.18419/darus-5064. |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1
Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Trained Models on Synthetic Permeability Fields, 3+1 Data Points", https://doi.org/10.18419/DARUS-5080, DaRUS, V1
Models are trained with Heat Plume Prediction. Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. The vanilla UNet can be applied directly end-to-end, just does not give very good results. For inference follow the guidelines of Heat Plume Prediction... |
Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport
Pelzer, Julia, 2025, "Datasets: 100 Heat Pumps + Synthetic Permeability Fields, Simulation - Raw, 101 Data Points", https://doi.org/10.18419/DARUS-5064, DaRUS, V1
This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 101 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8 km... |
Jun 26, 2025Surrogate models for groundwater flow simulations
Raw and prepared datasets and trained models for the publication "Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport", including randomized and real permeability fields, on different domain sizes (12.8kmx12.8km and double the size for scaling tests); DDUNet- and LGCNN architectures |
