CITATION.cff

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cff-version: 1.2.0
title: deepsysid
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Alexandra
    family-names: Baier
    affiliation: University of Stuttgart
    orcid: 'https://orcid.org/0000-0001-5609-3400'
    email: Alexandra.Baier@ki.uni-stuttgart.de
  - given-names: Daniel
    family-names: Frank
    email: Daniel.Frank@ki.uni-stuttgart.de
    affiliation: University of Stuttgart
    orcid: 'https://orcid.org/0000-0002-6730-2252'
identifiers:
  - type: doi
    value: 10.18419/darus-3455
    description: The DaRUS link of the software.
repository-code: 'https://github.com/AlexandraBaier/deepsysid'
repository: >-
  https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-3455
abstract: >-
  A system identification toolkit for multistep prediction
  using deep learning and hybrid methods.
keywords:
  - system identification
  - deep learning
license: MIT
doi: 10.18419/darus-3455
date-released: 2023