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Comment
. 2021 Mar 23;118(12):e2102169118.
doi: 10.1073/pnas.2102169118.

Learning active nematics one step at a time

Anna Frishman  1 , Kinneret Keren  2   3   4
Affiliations
Comment

Learning active nematics one step at a time

Anna Frishman et al. Proc Natl Acad Sci U S A. .
No abstract available

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Machine learning approaches for studying active-nematic systems. (A) The workflow for a machine learning approach for extracting hydrodynamic parameters in an active-nematic system characterized by a known model. (B) The workflow for a data-driven machine learning approach for forecasting the evolution of an active-nematic system from a short time sequence of nematic fields.
See this image and copyright information in PMC

Comment on

  • Machine learning active-nematic hydrodynamics.
    Colen J, Han M, Zhang R, Redford SA, Lemma LM, Morgan L, Ruijgrok PV, Adkins R, Bryant Z, Dogic Z, Gardel ML, de Pablo JJ, Vitelli V. Colen J, et al. Proc Natl Acad Sci U S A. 2021 Mar 9;118(10):e2016708118. doi: 10.1073/pnas.2016708118. Proc Natl Acad Sci U S A. 2021. PMID: 33653956 Free PMC article.

References

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    1. Doostmohammadi A., Ignés-Mullol J., Yeomans J. M., Sagués F., Active nematics. Nat. Commun. 9, 3246 (2018). - PMC - PubMed

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