Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points

DOI

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.

Please follow the instructions in README.md to prepare the data for training.

Identifier
DOI https://doi.org/10.18419/DARUS-5081
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5081
Provenance
Creator Pelzer, Julia ORCID logo; Verburg, Corné ORCID logo
Publisher DaRUS
Contributor Pelzer, Julia; Schulte, Miriam; Verburg, Corné; Heinlein, Alexander
Publication Year 2025
Funding Reference DFG EXC 2075 - 390740016
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Pelzer, Julia (University of Stuttgart); Schulte, Miriam (University of Stuttgart); Verburg, Corné (Delft University of Technology); Heinlein, Alexander (Delft University of Technology)
Representation
Resource Type Dataset
Format application/zip
Size 262083362; 80799424; 119455275; 93825683; 74101436; 51605459
Version 1.0
Discipline Chemistry; Construction Engineering and Architecture; Earth and Environmental Science; Engineering; Engineering Sciences; Environmental Research; Geosciences; Natural Sciences