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

Kannapinn, Maximilian ; Schäfer, Michael ; Weeger, Oliver (2024)
TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins.
In: Engineering Computations
doi: 10.1108/EC-11-2023-0855
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets. Design/methodology/approach: Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively. Findings: Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed. Originality/value: The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.

Typ des Eintrags: Artikel
Erschienen: 2024
Autor(en): Kannapinn, Maximilian ; Schäfer, Michael ; Weeger, Oliver
Art des Eintrags: Bibliographie
Titel: TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
Sprache: Englisch
Publikationsjahr: 5 Juli 2024
Verlag: Emerald
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Engineering Computations
DOI: 10.1108/EC-11-2023-0855
Kurzbeschreibung (Abstract):

Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets. Design/methodology/approach: Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively. Findings: Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed. Originality/value: The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.

Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Fachgebiet Cyber-Physische Simulation (CPS)
16 Fachbereich Maschinenbau > Fachgebiet für Numerische Berechnungsverfahren im Maschinenbau (FNB)
Exzellenzinitiative
Exzellenzinitiative > Graduiertenschulen
Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE)
Hinterlegungsdatum: 12 Aug 2024 06:26
Letzte Änderung: 12 Aug 2024 06:26
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