Trained Models on Real Permeability Fields, 4+1 Data Points

DOI

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 on raw data from https://doi.org/10.18419/darus-5065.

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

Identifier
DOI https://doi.org/10.18419/DARUS-5082
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5082
Provenance
Creator Pelzer, Julia ORCID logo
Publisher DaRUS
Contributor Pelzer, Julia; Schulte, Miriam
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)
Representation
Resource Type Dataset
Format application/zip
Size 186027030; 91852668
Version 1.0
Discipline Chemistry; Construction Engineering and Architecture; Earth and Environmental Science; Engineering; Engineering Sciences; Environmental Research; Geosciences; Natural Sciences