Journal
NATURE COMPUTATIONAL SCIENCE
Volume 1, Issue 5, Pages 313-320Publisher
SPRINGERNATURE
DOI: 10.1038/s43588-021-00072-5
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Funding
- UK Engineering and Physical Sciences Research Council [EP/M012492/1, NS/A000049/1, EP/P01268X/1, EP/T000414/1, EP/R018413/2, EP/P020720/2, EP/R034710/1, EP/R004889/1]
- British Heart Foundation [PG/15/91/31812, PG/13/37/30280, SP/18/6/33805]
- US National Institutes of Health [NIH R01-HL152256]
- European Research Council [ERC PREDICT-HF 864055]
- Wellcome Trust [WT 203148/Z/16/Z]
- Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre
- Moss Heart Foundation
- Cain Foundation
- NIH [R01 HL073021, R01 HL142504, R01 HL129077, R01 HL103723]
- Royal Academy of Engineering
- Lloyds Register Foundation
- US Department of Energy [DE-SC0021239]
- US Air Force Office of Scientific Research [FA9550-21-1-0084]
- U.S. Department of Energy (DOE) [DE-SC0021239] Funding Source: U.S. Department of Energy (DOE)
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Mathematical modeling and simulation are transitioning from development and analysis tools to operational monitoring, control, and decision support, utilizing digital twins that are currently difficult to scale. Challenges and opportunities for scaling digital twins are discussed, along with potential barriers to wider adoption of this technology.
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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