41 to 50 of 251 Results
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 3, 2025 - demoa
Walter, Johannes R.; Wochner, Isabell; Jacob, Marc; Stollenmaier, Katrin; Lerge, Patrick; Schmitt, Syn, 2022, "allmin: A Reduced Human All-Body Model", https://doi.org/10.18419/DARUS-2982, DaRUS, V3
A reduced all-body model parametrised using generic literature data for the geometry of the skeleton including attachment points for ligaments and muscles. This allmin model consists of a musculoskeletal model of the human body with 20 degrees of freedom actuated by 36 muscles. The model is prepared to run muscle-driven simulation. The file contain... |
May 28, 2025 - PN 6-8 (II)
Bauer, Ruben; Evers, Marina; Quang Ngo, Quynh; Reina, Guido; Frey, Steffen; Sedlmair, Michael, 2025, "Replication data for: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples", https://doi.org/10.18419/DARUS-4930, DaRUS, V1, UNF:6:3NivtJY91pyT7tXMB/YrJQ== [fileUNF]
This dataset contains the data and code for the publication: Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples. Code Repository A dynamic version of the code repository can be found at https://github.com/rbnbr/VoroParaSense. The version presented in this dataset corresponds to the version used in the corresponding pape... |
May 28, 2025 - PN 6-4
Schäfer, Noel; Künzel, Sebastian; Tilli, Pascal; Munz-Körner, Tanja; Vidyapu, Sandeep; Vu, Ngoc Thang; Weiskopf, Daniel, 2025, "Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering", https://doi.org/10.18419/DARUS-3909, DaRUS, V1
Source code of our extended visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset. Additionally, it is an improved version of our system that can be found here Instructions on how to use our system can be found in t... |
Apr 16, 2025 - Usability and Sustainability of Simulation Software
Tucciarone, Fabio, 2025, "Replication Data for: Greedy-kernel algorithms for data mapping in multiphysics simulations", https://doi.org/10.18419/DARUS-4790, DaRUS, V1
This data set contains all test cases and measurements referenced in the Bachelor's Thesis "Greedy-kernel algorithms for data mapping in multiphysics simulations". It also contains the modified code of preCICE and ASTE used to execute these test cases. |
