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Replication Data for: Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates.
No full textThis dataset contains all generated samples for a multi-objective optimization benchmark on a realistic building performance simulation problem. The samples are saved in JSON files. Every file contains the results of an independent optimization run.
The JSON log files are organized into two folders. AA_BPS_Benchmark contains the logs from the benchmark conducted using the Building Performance Simulation software TRNSYS for function evaluations. BB_Surrogate_Benchmark contains the files created using regression models for function evaluation, and CC_BPS_Random_Samples contains the logs used to train the surrogate models (see the README.txt file for more information)
OncoTUM models
No full textOncoTUM models
This repository hosts pretrained neural network models for OncoTUM,
a key software package within the umbrella project Onco* for modelling and numerical simulations of tumours. OncoTUM is designed to facilitate tumour segmentations from medical images, leveraging state-of-the-art deep learning techniques.
Purpose
The pretrained models in this repository are required for using the inference function of OncoTUM. These models have been trained on relevant datasets (BraTS 2020) to ensure
high accuracy and performance in tumor segmentation and its classification.
Usage
To utilise the inference functionality of OncoTUM, download the appropriate pretrained models from this repository and ensure they are correctly linked to the OncoTUM software package.
Detailed instructions for setup and integration can be found in the
OncoTUM documentation.
Content
In order to remain with most possible flexibility, the modality agnostic implementation allows to perform segmentation with a subset of the gold standard modalities.
Brain tumour segmentation
Full modal model: trained to all gold standard modalities (t1, t1gd, t2, flair).
Single modality model: trained to single modalities of the gold standard.
Null image: Empty image for training.
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Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess - Supplemental Material
No full textIn this supplemental material, we provide the appendix (mathematically exact propagation of uncertainty) and the video material for uncertainty-aware seasonal-trend decomposition based on loess (UASTL). This material complements the main document: The paper on Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess
Compressible Two-Phase One-Fluid Solver (compressiblePhaseChangeFoam)
No full textCompressibePhaseChangeFoam
This dataset contains the compressible two-phase, one-fluid solver called compressiblePhaseChangeFoam, developed and maintained by the Institute for Combustion Technology in Stuttgart.
Versions
This dataset contains the full git repository of the compressiblePhaseChangeFoam solver with the complete development history, starting in 2018. For some publications, older versions of the software have been used. While the newest version should produce the same results, it might be necessary for older cases to checkout the corresponding code version with the Git hash.
By providing the complete Git history, all versions can be checked out, and the development process can be recovered as well.
Build & Install Requirements
The solver is based on OpenFOAM v2012 and is tested for the following settings:
Operating system: Ubuntu 22.04
Compiler: gcc-9.5
OpenFOAM version: v2012 (compiled with gcc-9.5)
Third Party Libraries
The presented solver uses a tabulated thermophysical properties library called tabularThermo. This library is independently developed by the same authors as the solver; however, it is not publicly available.
To allow compilation and computation with this library, the tabularThermo git sub-module is included in the tar archive.
The solver also includes some unit testing with the Catch2 framework, which is included as a single header library.
The Catch2 library is published under the BSL-1.0 license.
Related Publications
A full list of related publications is given in the Meta Data tab.
J. W. Gärtner, A. Kronenburg, A. Rees, J. Sender, M. Oschwald, and G. Lamanna, "Numerical and Experimental Analysis of Flashing Cryogenic Nitrogen", International Journal of Multiphase Flow, vol 130, 2020, doi: 10.1016/j.ijmultiphaseflow.2020.103360
J. W. Gärtner, Y. Feng, A. Kronenburg, and O. T. Stein, "Numerical Investigation of Spray Collapse in GDI with OpenFOAM", Fluids, 6, 104, doi: 0.3390/fluids6030104
J. W. Gärtner, A. Kronenburg, A. Rees, and M. Oschwald, "Investigating 3-D Effects on Flashing Cryogenic Jets wiht Highly Resolved LES", Flow, Turbulence and Combustion, 2023, doi: 10.1007/s10494-023-00485-4
J. W. Gärtner and A. Kronenburg, "A Novel ELSA Model for Flash Evaporation", International Journal of Multiphase Flow, vol. 174, 2024, doi: 10.1016/j.ijmultiphaseflow.2024.104784
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Data repository for: Cutting a Wire with Non-Maximally Entangled States
No full textThis dataset contains the replication code for the publication titled "Cutting a Wire with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication.
For comprehensive instructions on using the provided data and code, please refer to the README.md file.
To reproduce the plots from the paper, execute the Jupyter notebook plots.ipynb with the data available in data.csv.
To generate new data, utilize the numerical_simulation.ipynb.
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Code for Faithful Embeddings for EL++ Knowledge Bases
No full textThis is the official pytorch implementation of the paper "Faithful embeddings for EL++ Knowledge Bases" published in ISWC 2022. The code was implemented based on el-embeddings.
The code can be used to reproduce the experiments on subsumption reasoning. To execute the code, follow the instructions in the README.md file. For more info, please check the paper. Please have no hesitation to contact the authors for any inquiries.</p
Dataset for NMF-based Analysis of Mobile Eye-Tracking Data
No full textThis mobile eye-tracking dataset consists of 27 recordings of three participants (all authors) walking through a small art gallery. Participants were instructed to attend individual paintings in specific orders, resulting in five distinct scanpath patterns.
The recordings' duration ranges from 50 to 205 seconds. Each recording comprises world video, gaze, fixations, saccades, blinks, and IMU data. Recordings were made with the Pupil Invisible eye-tracking glasses
D1244 sensor data (April 2024)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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Replication Data for: Biocatalytic stereocontrolled head-to-tail cyclizations of unbiased terpenes as a tool in chemoenzymatic synthesis
No full textThis data set contains all relevant simulation input files (topologies, coordinates, simulation parameters), generated simulation output (final configurations, time series of collective variables) together with scripts used for set-up and analysis of the umbrella sampling and double decoupling simulations
Image processing code for characterization of multiphase flow in porous media
No full textThis work utilizes microfluidic experiments to gather data captured as snapshots during the experiments. These snapshots provide real-time information and undergo image processing to derive the required data. Image processing involves several steps tailored to the investigations:
Making a reference image (mask): This process involves creating a reference image, or mask, to document the initial conditions. For instance, the porous domain is imaged when saturated with one phase to differentiate various areas containing different phases.
Reading and cutting images: Images showing changes in fluid volume fraction are selectively chosen and processed. Each image is read into MATLAB, and the area of interest is extracted.
Image segmentation: Labeling each pixel of the images is done via thresholding and edge detection.
Measuring parameters: Parameters like saturation, interfacial length, area, contact angle, and curvature are measured. These parameters play a crucial role in analyzing the experiments. The interfacial area is calculated through various formulations.
Calculating capillary pressure: Several forms of capillary pressure are calculated using information derived from the experiments.
REV-Scale Quantities: Parameters are upscaled to represent Representative Elementary Volume (REV)-scale values essential for continuum theories. REV-scale capillary pressure is derived from pore-scale values using appropriate averaging techniques.
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