TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
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.
Keywords
Acknowledgements
The authors acknowledge the support of the Graduate School CE within the Centre for Computational Engineering at the Technical University of Darmstadt and thank Minh Khang Pham for generating some of the employed data sets.
Citation
Kannapinn, M., Schäfer, M. and Weeger, O. (2024), "TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins", Engineering Computations, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EC-11-2023-0855
Publisher
:Emerald Publishing Limited
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