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Jose et al. MLST 2025 - Code, Data and Models
This dataset hosts the code, data and models needed for replication of the work mentioned in Jose et al. MLST 2025. In the paper, an ablation study is conducted to delineate the... -
QoI - Droplets
This dataset consists of a database of regular and distorted droplets with droplet clusters extracted from high-resolution shadowgraphy images in technical sprays. It serves... -
Nuisance - OOFObjs
This dataset contains the database of Out Of Focus Objects (OOFObjs)—currently mostly droplets and used for droplet sizing in shadowgraphy analysed sprays. The database... -
Nuisance - OOPObjs
This dataset contains the database of Out Of Plane Objects (OOPObjs) extracted from PIV (Mie scattering) measurements within an RQL-type combustor. The database is one... -
Background - Tracer particle regions
This dataset contains the database of image snippets of PIV tracer particles (i.e. Mie scattering) in the background, extracted from sooting flame measurements within an... -
QoI - Tracer particles
This dataset contains the database of image snippets of dense clusters of PIV tracer particles (i.e. Mie scattering) marking the reactants within sooting flame measurements... -
QoI - Soot filaments
This dataset contains the database of soot filaments extracted from PIV (Mie scattering) measurements, CFD simulations and generative models. The database is one subclass... -
Replication Data for: Super-resolution reconstruction of scalar fields from t...
README body { font-family: system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif; line-height: 1.5; padding: 1rem; max-width: 900px; margin:... -
Code for training and using the soot (instance) segmentation models
This dataset contains the necessary code for using our soot (instance) segmentation model used for segmenting soot filaments from PIV (Mie scattering) images. In the... -
Replication Data for: Super-resolution of turbulent velocity fields in two-wa...
The repository contains files required to reproduce the results. The three compressed filed are (i) torch_code, (ii) datasets, and (iii) experiments. Detailed files description... -
Data repository for "Loss Behavior in Supervised Learning With Entangled States"
Replication code and experiment result data for training Parameterized Quantum Circuits (PQCs) with entangled data. The experiments evaluate the structure of the loss landscape... -
Replication Data for: A parametric design integrated sampling and general tra...
This dataset contains the following data: scenes-Folder: the Grasshopper files, the the project files created by ModelToRad-component as well as the epw-file of the location.... -
AccessGuru
AccessGuru dataset contains over 3,500 real-world Web accessibility violations collected from 448 diverse websites across domains such as health, education, government, news,... -
Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs
This is a Pytorch implementation of the paper Shrinking Embeddings for Hyper-relational Knowledge Graphs published in ACL'23. This code is used to reproduce the experiments of... -
Replication Data for NestE: Modeling Nested Relational Structures for Knowled...
This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding... -
Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves ...
This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen absorption in C14 (hexagonal) and C15 (cubic)... -
Trained Models on Real Permeability Fields, 4+1 Data Points
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... -
Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points
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... -
Trained Models on Synthetic Permeability Fields, 3+1 Data Points
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... -
Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 ...
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...
