Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Applied mathematics

Synthesizing domain science with machine learning

To understand whether or not the design of machine learning systems can integrate domain expertise, a recent work proposes methodologies to synthesize domain science with machine learning, which shows added benefits.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Three approaches to dimensional analysis.

References

  1. Sutton, R. The bitter lesson. Incomplete Ideas (13 March 2013); http://incompleteideas.net/IncIdeas/BitterLesson.html

  2. Brown, T. et al. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).

    Google Scholar 

  3. Bakarji, J., Callaham, J., Brunton, S. L. & Kutz, J. N. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00355-5 (2022).

  4. del Rosario, Z., Lee, M. & Iaccarino, G. SIAM-ASA J. Uncertain. 7, 232–259 (2019).

    Article  Google Scholar 

  5. Shen, W. & Lin, D. K. J. Technometrics 60, 79–89 (2018).

    Article  MathSciNet  Google Scholar 

  6. Fukami, K. & Taira, K. In 74th Annual Meeting of the APS Division of Fluid Dynamics abstr. A31.00004 (APS, 2021).

  7. Zhang, J. & Ghanem, B. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1828–1837 (IEEE, 2018).

  8. del Rosario, M. & Ding, Z. IEEE Trans. Wirel. Commun. https://doi.org/10.1109/TWC.2022.3202750 (2022).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zachary del Rosario.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

del Rosario, Z., del Rosario, M. Synthesizing domain science with machine learning. Nat Comput Sci 2, 779–780 (2022). https://doi.org/10.1038/s43588-022-00358-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-022-00358-2

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing