deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning
A. Baier, and D. Frank. Software, (2023)Related to: Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727. arXiv: abs/2103.06727.
DOI: 10.18419/darus-3455
Abstract
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: deepsysid download 4dof-sim-ship, deepsysid session --enable-cuda progress.json NEW, deepsysid session --enable-cuda --reportin=progress.json progress.json TEST_BEST. The most current version of this software is available on GitHub.
Related to: Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727. arXiv: abs/2103.06727
%0 Generic
%1 baier2023deepsysid
%A Baier, Alexandra
%A Frank, Daniel
%D 2023
%K EXC2075 PN4 PN4-7 curated
%R 10.18419/darus-3455
%T deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning
%X deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: deepsysid download 4dof-sim-ship, deepsysid session --enable-cuda progress.json NEW, deepsysid session --enable-cuda --reportin=progress.json progress.json TEST_BEST. The most current version of this software is available on GitHub.
@misc{baier2023deepsysid,
abstract = {deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: deepsysid download 4dof-sim-ship, deepsysid session --enable-cuda progress.json NEW, deepsysid session --enable-cuda --reportin=progress.json progress.json TEST_BEST. The most current version of this software is available on GitHub. },
added-at = {2024-01-17T16:01:18.000+0100},
affiliation = {Baier, Alexandra/Universität Stuttgart, Frank, Daniel/Universität Stuttgart},
author = {Baier, Alexandra and Frank, Daniel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29768defd15033d9f0dcf05136cf205e6/simtech},
doi = {10.18419/darus-3455},
howpublished = {Software},
interhash = {bdd0ba0fd06024e5606c28ad23efd3a9},
intrahash = {9768defd15033d9f0dcf05136cf205e6},
keywords = {EXC2075 PN4 PN4-7 curated},
note = {Related to: Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727. arXiv: abs/2103.06727},
orcid-numbers = {Baier, Alexandra/0000-0001-5609-3400, Frank, Daniel/0000-0002-6730-2252},
timestamp = {2024-02-26T18:00:29.000+0100},
title = {deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning},
year = 2023
}
