TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins

Maximilian Kannapinn, Michael Schäfer, Oliver Weeger

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DOI: 10.1108/ec-11-2023-0855
Published: 2024-07-05
Container: Engineering Computations
Publisher: Emerald
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1108/ec-11-2023-0855,
  title = {TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins},
  author = {Maximilian Kannapinn and Michael Schäfer and Oliver Weeger},
  year = {2024},
  journal = {Engineering Computations},
  doi = {10.1108/ec-11-2023-0855},
  url = {https://doi.org/10.1108/ec-11-2023-0855}
}

RIS

TY  - JOUR
TI  - TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
AU  - Maximilian Kannapinn
AU  - Michael Schäfer
AU  - Oliver Weeger
PY  - 2024
JO  - Engineering Computations
DO  - 10.1108/ec-11-2023-0855
UR  - https://doi.org/10.1108/ec-11-2023-0855
ER  -

APA

Kannapinn, M., Schäfer, M., & Weeger, O. (2024). TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins. Engineering Computations. https://doi.org/10.1108/ec-11-2023-0855

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crossref · retrieved 2026-09-26T09:51:25.551Z