Skip to main content

Predictive Digital Twins: Where Dynamic Data-Driven Learning Meets Physics-Based Modeling

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12312)

Abstract

A digital twin is an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. An asset-specific model is a powerful tool to underpin intelligent automation and drive key decisions. The formulations and methods of dynamic data-driven application systems (DDDAS) have a key role to play in the tasks of inference, assimilation, prediction, control, and planning that enable the digital twin paradigm. Of particular importance is a tight feedback loop between models and data, which has long been a central concept in DDDAS. This keynote talk presents an approach to create, update, and deploy data-driven physics-based digital twins. We demonstrate the approach through the development of a structural digital twin for a custom-built unmanned aerial vehicle.

Keywords

  • Digital twin
  • DDDAS
  • Unmanned aerial vehicle

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-61725-7_1
  • Chapter length: 5 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-61725-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

Reference

  • Kapteyn, M., Knezevic, D., Willcox, K.: Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. In: Proceedings of 2020 AIAA SciTech Forum & Exhibition, Orlando, FL (2020)

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support of AFOSR grant FA9550-16-1-0108 under the Dynamic Data Driven Application Systems Program and the SUTD-MIT International Design Center.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karen E. Willcox .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Kapteyn, M.G., Willcox, K.E. (2020). Predictive Digital Twins: Where Dynamic Data-Driven Learning Meets Physics-Based Modeling. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61725-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61724-0

  • Online ISBN: 978-3-030-61725-7

  • eBook Packages: Computer ScienceComputer Science (R0)