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Scaling digital twins from the artisanal to the industrial

Nature Computational Science volume 1, pages 313–320 (2021)Cite this article

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Abstract

Mathematical modeling and simulation are moving from being powerful development and analysis tools towards having increased roles in operational monitoring, control and decision support, in which models of specific entities are continually updated in the form of a digital twin. However, current digital twins are largely the result of bespoke technical solutions that are difficult to scale. We discuss two exemplar applications that motivate challenges and opportunities for scaling digital twins, and that underscore potential barriers to wider adoption of this technology.

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Fig. 1: A schematic overview of a digital-twin approach as applied to a dynamically updated heart model.
Fig. 2: A schematic of the length and timescale bridging modeling challenges in cardiology and aerospace systems.
Fig. 3: Digital twins are created for multiple physical assets such as UAVs.

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Acknowledgements

S.N. acknowledges support from the UK Engineering and Physical Sciences Research Council (grant nos. EP/M012492/1, NS/A000049/1 and EP/P01268X/1), the British Heart Foundation (grant nos. PG/15/91/31812, PG/13/37/30280, SP/18/6/33805), US National Institutes of Health (grant no. NIH R01-HL152256), European Research Council (grant no. ERC PREDICT-HF 864055), Wellcome Trust (grant no. WT 203148/Z/16/Z) and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. M.S. acknowledges the Moss Heart Foundation, the Cain Foundation and NIH grants (grant nos. R01 HL073021, R01 HL142504, R01 HL129077 and R01 HL103723). M.G. acknowledges support from the UK Engineering and Physical Sciences Research Council (grant nos. EP/T000414/1, EP/R018413/2, EP/P020720/2, EP/R034710/1, EP/R004889/1), as well as a Research Chair supported by the Royal Academy of Engineering and Lloyds Register Foundation. K.W. acknowledges support from the US Department of Energy (grant no. DE-SC0021239) and the US Air Force Office of Scientific Research (grant no. FA9550-21-1-0084). The authors thank G. Foss of Texas Advanced Computing Center (TACC) for creating the heart image in Fig. 2.

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Authors and Affiliations

  1. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Steven A. Niederer

  2. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA

    Michael S. Sacks & Karen Willcox

  3. Department of Engineering, University of Cambridge, Cambridge, UK

    Mark Girolami

  4. The Alan Turing Institute, London, UK

    Mark Girolami

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Peer review information Nature Computational Science thanks Steven Brunton, Kristi Morgansen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Niederer, S.A., Sacks, M.S., Girolami, M. et al. Scaling digital twins from the artisanal to the industrial. Nat Comput Sci 1, 313–320 (2021). https://doi.org/10.1038/s43588-021-00072-5

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