311 to 320 of 2,123 Results
Jul 15, 2025 - Usability and Sustainability of Simulation Software
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 9, 2025 - 2022_DFG_IKEPa
Reichenbach, Thomas; Clar, Johannes; Pott, Andreas; Verl, Alexander, 2025, "Replication Data for: Experimental Investigation on Adaptive Preload Control for Cable-Driven Parallel Robots", https://doi.org/10.18419/DARUS-4614, DaRUS, V1, UNF:6:yIeeojFX6ZB47EKsVBPaaA== [fileUNF]
Measurement data and analysis scripts to investigate a novel adaptive preload control (APC) for cable-driven parallel robots. The measurement data is provided as csv files and consists of data from two devices: the robot control system and the coordinate measuring system (laser tracker). The data from the robot control system is stored in the "expe... |
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... |
Jul 2, 2025 - Publication: Intermittent flow paths in biofilms grown in a microfluidic channel
Bozkurt, Kerem; Lohrmann, Christoph; Weinhardt, Felix; Hanke, Daniel; Hopp, Raphael; Gerlach, Robin; Holm, Christian; Class, Holger, 2025, "Data for: Intermittent flow paths in biofilms grown in a microfluidic channel", https://doi.org/10.18419/DARUS-4314, DaRUS, V1
Content: This dataset includes images of 10 different biofilm growth experiments to observe intermittent flow paths in a microfluidic channel. All images taken by optical microscopy per five minutes. The experiments took in avarage 4-5 days. The published data is the raw images of the experiments. The images names were related to the timestamps as... |
Jun 30, 2025 - Data Analytics in Engineering
Keshav, Sanath; Herb, Julius; Fritzen, Felix, 2025, "Supplemental data for "Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees"", https://doi.org/10.18419/DARUS-5120, DaRUS, V1
This repository contains supplemental data for the article "Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees", accepted for publication in GAMM-Mitteilungen by Sanath Keshav, Julius Herb, and Felix Fritzen [1]. The data contained in this DaRUS repository acts as an exte... |
