Model-based deep reinforcement learning for accelerated learning from flow simulations

DOI

This dataset accompanies the article titled "Model-based deep reinforcement learning for accelerated learning from flow simulations". The archives contained in this dataset are: - data.tar.gz: the main research results (trajectory data, snapshots of policy and value networks, log files of the training process, results of repeated independent runs (seeds)) - mbdrl-main-branch-Apr2024.zip: a snapshot of the code repository used to create all derived results presented in the article; the repository is also available at https://github.com/JanisGeise/MB_DRL_for_accelerated_learning_from_CFD - drlfoam-fork-mbdrl-Apr2024.zip: drlFoam is the library implementing the DRL infrastructure and logic; this archive contains a snapshot of the drlFoam fork used in this work; the fork adds model-based learning to drlFoam; the fork is also available at https://github.com/JanisGeise/drlfoam/tree/mb_drl

Identifier
DOI https://doi.org/10.23728/b2share.85ab8f3f68724372b83babbdaca85910
Source https://b2share.eudat.eu/records/85ab8f3f68724372b83babbdaca85910
Metadata Access https://b2share.eudat.eu/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.eudat.eu:b2rec/85ab8f3f68724372b83babbdaca85910
Provenance
Creator Andre Weiner; Janis Geise
Publisher EUDAT B2SHARE
Publication Year 2024
Rights Creative Commons Attribution (CC-BY); info:eu-repo/semantics/openAccess
OpenAccess true
Contact andre.weiner(at)tu-dresden.de
Representation
Format gz; zip
Size 17.2 GB; 3 files
Discipline 3.4.11 → Physics → Fluid dynamics